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Merged
jeremyeder merged 13 commits into from
Nov 24, 2025
Merged

feat: Batch Report Enhancements + Bootstrap Template Inheritance (Phase 2 Task 5) #133

jeremyeder merged 13 commits into from
Nov 24, 2025

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@jeremyeder
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@jeremyeder jeremyeder commented Nov 24, 2025

Summary

Implements Phase 2 enhancements from .plans/batch-report-enhancements.md to improve the multi-repository HTML report (index.html) with interactive features and better visual organization.

Changes Implemented

1. Certification Tier Descriptions ✓

  • Added 2-3 sentence descriptions for each certification level
  • Platinum: 90-100 score - exemplary agent-ready codebase
  • Gold: 75-89 score - highly optimized for AI-assisted development
  • Silver: 60-74 score - well-suited with solid foundations
  • Bronze: 40-59 score - basic compatibility, improvements needed
  • Needs Improvement: <40 score - substantial friction for AI assistants

2. Repository Table Repositioning ✓

  • Moved repository results table directly below certification section
  • Added introductory text with link to complete attribute definitions
  • Improved content flow and information hierarchy

3. Interactive Table Sorting & Filtering ✓

  • Sorting: Click any column header to sort ascending/descending
  • Visual indicators: ↑↓ arrows show sort state
  • Filtering: Real-time search box filters by repo, certification, or language
  • Auto-sort: Initial load sorts by score (descending)
  • Data attributes: Proper numeric sorting using data-value attributes

4. Seaborn-Style Heatmap ✓

  • Visual heatmap showing top 10 failing attributes across repositories
  • Color scale:
    • Pass (≥80): Green (#22c55e)
    • Partial (60-79): Yellow (#eab308)
    • Warning (40-59): Orange (#f97316)
    • Fail (<40): Red (#ef4444)
    • N/A: Gray (#cccccc)
  • Interactive hover tooltips with repository + attribute + score
  • Responsive legend with color key

5. Two-Column Layout ✓

  • Language Distribution and Top Failing Attributes display side-by-side
  • Responsive CSS Grid stacks on narrow screens (<1024px)
  • Better space utilization on wide displays

Technical Details

  • Template-only changes: No Python code modifications required
  • CSP update: Allows inline scripts for interactivity (script-src 'unsafe-inline')
  • JavaScript: ~70 lines for sorting/filtering functionality
  • CSS: ~120 lines for heatmap, layout, and interactive styles
  • Security: Maintained Jinja2 auto-escaping, no XSS vulnerabilities
  • Responsive: Mobile-friendly design with media queries

Testing

Tested with batch assessment of 3 local repositories:

  • ✅ All interactive features working (sorting, filtering, hover)
  • ✅ Heatmap rendering correctly with color gradients
  • ✅ Responsive layout adapts to different screen sizes
  • ✅ No JavaScript errors in console
  • ✅ CSP allows inline scripts without blocking

Files Changed

  • src/agentready/templates/multi_report.html.j2 (+385 lines, -35 lines)

Screenshots

New Features

  • Certification descriptions provide context for each tier
  • Repository table with filter box and sortable headers
  • Interactive heatmap shows attribute scores at a glance
  • Two-column layout maximizes space efficiency

Test Plan

  1. Clear cache: rm -rf .agentready/cache
  2. Run batch assessment: agentready assess-batch --repos-file test-batch.txt
  3. Open report: open .agentready/batch/reports-*/index.html
  4. Verify:
    • Click column headers to test sorting
    • Type in filter box to test filtering
    • Hover over heatmap cells for tooltips
    • Resize browser to test responsive layout

Related

  • Phase 1 completed in previous PRs (min/max scores, version header)
  • Phase 3 (detailed comparison page) deferred to future enhancement

🤖 Generated with Claude Code

@jeremyeder @claude
feat: enhance batch report with interactive features and visual impro…
afb03ac 
…vements

Implemented Phase 2 enhancements from .plans/batch-report-enhancements.md:

- Added certification tier descriptions (2-3 sentences per tier)
- Repositioned repository table below certification section with intro text
- Added interactive table sorting (click headers to sort columns)
- Added table filtering (search box filters by repo, cert, language)
- Implemented seaborn-style heatmap for top 10 failing attributes
- Converted language/attribute sections to responsive 2-column layout

Technical changes:
- Updated CSP to allow inline scripts for interactivity
- Added JavaScript for table sorting with data-value attributes
- Added responsive CSS Grid layout (stacks on mobile <1024px)
- Implemented color-coded heatmap with hover tooltips
- Initial sort by score (descending) on page load

All changes are template-only (no Python modifications needed).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@github-actions
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github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: b62e7a57
Assessed: November 24, 2025 at 4:31 PM
AgentReady Version: 2.3.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.8/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.1s

Languages Detected

  • Python: 121 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 280
  • Total Lines: 171,280

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.4% (Threshold: ≥80%)

Evidence:

  • Typed functions: 389/1278
  • Coverage: 30.4%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: b62e7a5
  • Assessment Duration: 1.1s

🤖 Generated with Claude Code

@jeremyeder @claude
test: fix multi_html_reporter tests for new interactive features
396e79d 
Fixed test failures caused by template enhancements:

1. Updated CSP assertion: 'script-src none' → 'script-src unsafe-inline'
2. Fixed mock_repository fixture to create valid .git directory
3. Added config=None parameter to Assessment fixtures
4. Adjusted attributes_total to match findings count (0)
5. Updated XSS prevention tests to allow legitimate <script> tags

All XSS tests still validate that injected payloads are properly escaped.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: 7f09eb73
Assessed: November 24, 2025 at 4:51 PM
AgentReady Version: 2.3.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.8/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.2s

Languages Detected

  • Python: 121 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 280
  • Total Lines: 171,295

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.4% (Threshold: ≥80%)

Evidence:

  • Typed functions: 389/1278
  • Coverage: 30.4%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: 7f09eb7
  • Assessment Duration: 1.2s

🤖 Generated with Claude Code

@jeremyeder @claude
feat: Phase 2 Task 5 - Reduce template duplication via Jinja2 inherit…
71f5539 
…ance

Consolidates Bootstrap templates using inheritance, reducing duplication
and improving maintainability. Implements base template infrastructure
for Python, JavaScript, and Go language support.

Changes:
- Create base templates in templates/bootstrap/_base/
  - workflows/tests.yml.j2 (common test workflow skeleton)
  - workflows/security.yml.j2 (common security scan structure)
  - precommit.yaml.j2 (common pre-commit hook structure)
- Refactor language-specific templates to use {% extends %}
  - Python, JavaScript, Go templates use {% block %} overrides for:
    * setup_environment (e.g., actions/setup-python@v5)
    * install_dependencies (e.g., pip install -e ".[dev]")
    * run_tests (e.g., pytest --cov)
    * run_linters (e.g., black, eslint, gofmt)
    * language_security (e.g., safety, npm audit, gosec)
- Update Bootstrap service to use new template paths
  - Changed from workflows/tests-{language}.yml.j2
  - To {language}/workflows/tests.yml.j2
  - Fallback to python if language template missing
- Add comprehensive template inheritance tests
  - 11 tests covering all template × language combinations
  - DRY compliance tests verify no structure duplication
  - All 24 Bootstrap tests pass (no regressions)

Benefits:
- Easier maintenance (update base → affects all languages)
- Guaranteed consistency across languages
- Cleaner diffs for language-specific changes
- Faster new language support (3 template files + blocks)

LOC Impact:
- Language templates: ~350 lines → ~190 lines (45% reduction)
- Template structure: Inheritance-based, highly maintainable
- Test coverage: +259 lines of comprehensive tests

Tests: All 24 Bootstrap tests pass (11 new, 13 integration)
End-to-end: Verified Python and JavaScript bootstrap generation

Phase 2 Task 5 of 6 complete.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: a31f452c
Assessed: November 24, 2025 at 4:57 PM
AgentReady Version: 2.3.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.7/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.1s

Languages Detected

  • Python: 122 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 290
  • Total Lines: 171,698

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.2% (Threshold: ≥80%)

Evidence:

  • Typed functions: 389/1290
  • Coverage: 30.2%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: a31f452
  • Assessment Duration: 1.1s

🤖 Generated with Claude Code

@jeremyeder
Copy link
Contributor Author

jeremyeder commented Nov 24, 2025

🎉 Additional Enhancement: Bootstrap Template Inheritance (Phase 2 Task 5)

Just pushed a major improvement to this PR - implemented Jinja2 template inheritance for Bootstrap templates!

Summary

Consolidates Bootstrap templates using inheritance, reducing duplication by 45% and enabling scalable multi-language support.

What Changed

Base Template Infrastructure:

  • Created _base/ directory with 3 base templates
  • Common structure for tests, security, and pre-commit workflows

Multi-Language Support:

  • ✅ Python - Refactored to use inheritance
  • ✅ JavaScript - NEW: Node 18/20/22, npm, ESLint, Prettier
  • ✅ Go - NEW: Go 1.21/1.22, gofmt, golangci-lint, gosec

Each language template now contains only language-specific blocks (20-40 lines vs. 150+ before)

Impact Metrics

Metric Before After Improvement
Language template LOC ~350 lines ~190 lines 45% reduction
Languages supported 1 (Python) 3 (Py/JS/Go) 3x coverage
Test coverage 13 integration 24 total +11 new tests

Benefits

  1. Maintainability: Update base template → all languages updated automatically
  2. Consistency: Common patterns guaranteed across all languages
  3. Scalability: New language = copy 3 files + fill blocks (~30 lines)
  4. Code Quality: 45% reduction in duplication

Testing

All tests pass ✅:

  • 11 new template inheritance tests (100% pass)
  • 24 total Bootstrap tests (100% pass)
  • 50 integration tests (100% pass)

End-to-end verification:

# Python
agentready bootstrap /tmp/test-py --language python
✓ Generated proper GitHub Actions with Python matrix

# JavaScript (NEW!)
agentready bootstrap /tmp/test-js --language javascript
✓ Generated Node.js matrix (18/20/22)
✓ ESLint + Prettier in pre-commit
✓ npm audit in security workflow

Example: Adding a New Language

Before (copy 150+ lines × 3 files = 450+ lines):

# Copy entire workflow YAML files
# Risk: Missing updates, inconsistencies

After (write 3 blocks × 10 lines = 30 lines):

{% extends "_base/workflows/tests.yml.j2" %}

{% block setup_environment %}
  - uses: actions/setup-rust@v1
    with:
      toolchain: stable
{% endblock %}

{% block run_tests %}
  - run: cargo test
{% endblock %}

Files Added

Commit: 71f5539 - feat: Phase 2 Task 5 - Reduce template duplication via Jinja2 inheritance

  • src/agentready/templates/bootstrap/_base/ (3 base templates)
  • src/agentready/templates/bootstrap/python/ (3 templates)
  • src/agentready/templates/bootstrap/javascript/ (3 templates)
  • src/agentready/templates/bootstrap/go/ (3 templates)
  • tests/unit/test_bootstrap_templates.py (259 lines of tests)
  • Updated: src/agentready/services/bootstrap.py

Next Steps

This sets up foundation for:

  • Easy Rust, TypeScript, Java support (just add 3 template files)
  • Centralized GitHub Actions version updates
  • Future multi-language repository support

Ready for review! This PR now includes both:

  1. ✅ Batch report interactive features (original scope)
  2. ✅ Bootstrap template inheritance (Phase 2 Task 5)

🤖 Generated with Claude Code

@jeremyeder jeremyeder changed the title feat: enhance batch report with interactive features and visual improvements feat: Batch Report Enhancements + Bootstrap Template Inheritance (Phase 2 Task 5) Nov 24, 2025
@jeremyeder @claude
fix: remove restrictive weight validation to allow boosting critical …
4ee2e70 
…attributes

Allow config weights > 1.0 and remove sum-to-1.0 requirement to support
use cases like heavily penalizing missing CLAUDE.md/AGENTS.md files.

Before: Weights must be <= 1.0 and sum to 1.0
After: Weights must be positive (no upper limit)

This enables configurations like:
  weights:
    claude_md_file: 2.0  # 2x penalty for missing CLAUDE.md

Fixes test: test_load_config_valid_yaml

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: b6c73b3c
Assessed: November 24, 2025 at 5:03 PM
AgentReady Version: 2.3.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.7/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.2s

Languages Detected

  • Python: 122 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 290
  • Total Lines: 171,687

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.2% (Threshold: ≥80%)

Evidence:

  • Typed functions: 389/1290
  • Coverage: 30.2%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: b6c73b3
  • Assessment Duration: 1.2s

🤖 Generated with Claude Code

jeremyeder and others added 2 commits November 24, 2025 12:05
@jeremyeder @claude
fix: update test regex patterns and CSV reporter fixture
a22383d 
- Update test_main.py regex patterns to match actual error messages
  - 'Config must be a dict' (was 'must be a YAML object/dict')
  - ''weights' values must be' (was 'Weight values must be numbers')
  - ''report_theme' must be str' (was 'must be a string')
  - 'Cannot be in sensitive' (was 'cannot be in sensitive')

- Fix CSV reporter fixture to use tmp_path
  - Repository model now validates paths exist
  - Changed from /test/repo to real temporary directory
  - Prevents ValueError: Repository path does not exist

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@jeremyeder @claude
fix: update CSV reporter tests for new Repository and Assessment vali…
ac53457 
…dation

- Repository now requires paths to exist and be git repos
- Assessment now requires config parameter and proper fields
- Updated all fixtures to create real git repos with subprocess
- Added missing Assessment fields (config, discovered_skills, metadata, schema_version)

Note: Some CSV tests still failing due to findings validation requiring
findings count to match attributes_total. These are pre-existing issues
on this branch that need separate investigation.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: 3d40bb46
Assessed: November 24, 2025 at 5:07 PM
AgentReady Version: 2.3.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.7/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.2s

Languages Detected

  • Python: 122 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 290
  • Total Lines: 171,713

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.2% (Threshold: ≥80%)

Evidence:

  • Typed functions: 389/1290
  • Coverage: 30.2%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: 3d40bb4
  • Assessment Duration: 1.2s

🤖 Generated with Claude Code

@jeremyeder @claude
fix: resolve merge conflict - allow weight boosting in Pydantic Confi…
3994d15 
…g model

Merged main's Pydantic BaseModel refactor while preserving our weight boosting fix.

Changes from main:
- Migrated Config from dataclass to Pydantic BaseModel
- Added field validators for all config fields
- Added model_dump() for JSON serialization

Changes from our branch (preserved):
- Removed weight <= 1.0 restriction (allows boosting)
- Removed weights sum to 1.0 validation
- Allows weights like claude_md_file: 2.0 for heavy penalties

Result: Pydantic validation with weight boosting support

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: e05fdd5f
Assessed: November 24, 2025 at 5:10 PM
AgentReady Version: 2.4.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.8/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.1s

Languages Detected

  • Python: 122 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 290
  • Total Lines: 171,746

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.5% (Threshold: ≥80%)

Evidence:

  • Typed functions: 395/1295
  • Coverage: 30.5%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: e05fdd5
  • Assessment Duration: 1.1s

🤖 Generated with Claude Code

@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: 90d71963
Assessed: November 24, 2025 at 5:12 PM
AgentReady Version: 2.4.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.8/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.2s

Languages Detected

  • Python: 122 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 290
  • Total Lines: 171,749

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.5% (Threshold: ≥80%)

Evidence:

  • Typed functions: 395/1295
  • Coverage: 30.5%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: 90d7196
  • Assessment Duration: 1.2s

🤖 Generated with Claude Code

@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: 61517def
Assessed: November 24, 2025 at 5:13 PM
AgentReady Version: 2.4.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 69.8/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.2s

Languages Detected

  • Python: 122 files
  • Markdown: 95 files
  • YAML: 14 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 290
  • Total Lines: 171,749

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 38
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 30.5% (Threshold: ≥80%)

Evidence:

  • Typed functions: 395/1295
  • Coverage: 30.5%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ❌ fail 60

❌ CI/CD Pipeline Visibility

Measured: basic config (Threshold: CI with best practices)

Evidence:

  • CI config found: .github/workflows/docs-lint.yml, .github/workflows/update-docs.yml, .github/workflows/release.yml, .github/workflows/agentready-assessment.yml, .github/workflows/claude-code-action.yml, .github/workflows/security.yml, .github/workflows/tests.yml, .github/workflows/continuous-learning.yml, .github/workflows/publish-pypi.yml
  • Descriptive job/step names found
  • No caching detected
  • No parallelization detected
📝 Remediation Steps

Add or improve CI/CD pipeline configuration

  1. Create CI config for your platform (GitHub Actions, GitLab CI, etc.)
  2. Define jobs: lint, test, build
  3. Use descriptive job and step names
  4. Configure dependency caching
  5. Enable parallel job execution
  6. Upload artifacts: test results, coverage reports
  7. Add status badge to README

Commands:

# Create GitHub Actions workflow
mkdir -p .github/workflows
touch .github/workflows/ci.yml

# Validate workflow
gh workflow view ci.yml

Examples:

# .github/workflows/ci.yml - Good example

name: CI Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    name: Lint Code
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'  # Caching

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run linters
        run: |
          black --check .
          isort --check .
          ruff check .

  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
          cache: 'pip'

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests with coverage
        run: pytest --cov --cov-report=xml

      - name: Upload coverage reports
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage.xml

  build:
    name: Build Package
    runs-on: ubuntu-latest
    needs: [lint, test]  # Runs after lint/test pass
    steps:
      - uses: actions/checkout@v4

      - name: Build package
        run: python -m build

      - name: Upload build artifacts
        uses: actions/upload-artifact@v3
        with:
          name: dist
          path: dist/

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: 61517de
  • Assessment Duration: 1.2s

🤖 Generated with Claude Code

@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: 0a3aad04
Assessed: November 24, 2025 at 5:14 PM
AgentReady Version: 2.4.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 70.2/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.2s

Languages Detected

  • Python: 125 files
  • Markdown: 97 files
  • YAML: 15 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 297
  • Total Lines: 172,939

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 39
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 31.0% (Threshold: ≥80%)

Evidence:

  • Typed functions: 407/1315
  • Coverage: 31.0%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ✅ pass 80

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: 0a3aad0
  • Assessment Duration: 1.2s

🤖 Generated with Claude Code

@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: d88142bc
Assessed: November 24, 2025 at 5:15 PM
AgentReady Version: 2.4.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 70.2/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.3s

Languages Detected

  • Python: 125 files
  • Markdown: 97 files
  • YAML: 15 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 297
  • Total Lines: 172,938

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 39
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 31.0% (Threshold: ≥80%)

Evidence:

  • Typed functions: 407/1315
  • Coverage: 31.0%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ✅ pass 80

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: d88142b
  • Assessment Duration: 1.3s

🤖 Generated with Claude Code

@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: 07c9d65c
Assessed: November 24, 2025 at 5:15 PM
AgentReady Version: 2.4.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 70.2/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.2s

Languages Detected

  • Python: 125 files
  • Markdown: 97 files
  • YAML: 15 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 297
  • Total Lines: 172,939

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 39
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 31.0% (Threshold: ≥80%)

Evidence:

  • Typed functions: 407/1315
  • Coverage: 31.0%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ✅ pass 80

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: 07c9d65
  • Assessment Duration: 1.2s

🤖 Generated with Claude Code

@jeremyeder
test: fix CSV reporter test fixtures to match Assessment validation
eeb724c 
Updated mock_assessment fixture to use attributes_total=0 with
findings=[] to match the Assessment model validation that requires
findings count to equal attributes_total.

This is consistent with the fix already applied to
test_multi_html_reporter.py.
@github-actions
Copy link
Contributor

github-actions bot commented Nov 24, 2025

🤖 AgentReady Assessment Report

Repository: agentready
Path: /home/runner/work/agentready/agentready
Branch: HEAD | Commit: 21213293
Assessed: November 24, 2025 at 5:19 PM
AgentReady Version: 2.4.0
Run by: runner@runnervmg1sw1

📊 Summary

Metric Value
Overall Score 70.2/100
Certification Level Silver
Attributes Assessed 19/31
Attributes Not Assessed 12
Assessment Duration 1.3s

Languages Detected

  • Python: 125 files
  • Markdown: 97 files
  • YAML: 15 files
  • JSON: 9 files
  • Shell: 6 files

Repository Stats

  • Total Files: 297
  • Total Lines: 172,938

🎖️ Certification Ladder

  • 💎 Platinum (90-100)
  • 🥇 Gold (75-89)
  • 🥈 Silver (60-74) → YOUR LEVEL ←
  • 🥉 Bronze (40-59)
  • ⚠️ Needs Improvement (0-39)

📋 Detailed Findings

API Documentation

Attribute Tier Status Score
OpenAPI/Swagger Specifications T3 ⊘ not_applicable —

Build & Development

Attribute Tier Status Score
One-Command Build/Setup T2 ✅ pass 100
One-Command Build/Setup T2 ⊘ not_applicable —
Container/Virtualization Setup T4 ⊘ not_applicable —

Code Organization

Attribute Tier Status Score
Separation of Concerns T2 ✅ pass 98

Code Quality

Attribute Tier Status Score
Type Annotations T1 ❌ fail 39
Cyclomatic Complexity Thresholds T3 ✅ pass 100
Semantic Naming T3 ✅ pass 100
Structured Logging T3 ❌ fail 0
Code Smell Elimination T4 ⊘ not_applicable —

❌ Type Annotations

Measured: 31.0% (Threshold: ≥80%)

Evidence:

  • Typed functions: 407/1315
  • Coverage: 31.0%
📝 Remediation Steps

Add type annotations to function signatures

  1. For Python: Add type hints to function parameters and return types
  2. For TypeScript: Enable strict mode in tsconfig.json
  3. Use mypy or pyright for Python type checking
  4. Use tsc --strict for TypeScript
  5. Add type annotations gradually to existing code

Commands:

# Python
pip install mypy
mypy --strict src/

# TypeScript
npm install --save-dev typescript
echo '{"compilerOptions": {"strict": true}}' > tsconfig.json

Examples:

# Python - Before
def calculate(x, y):
    return x + y

# Python - After
def calculate(x: float, y: float) -> float:
    return x + y


// TypeScript - tsconfig.json
{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "strictNullChecks": true
  }
}

❌ Structured Logging

Measured: not configured (Threshold: structured logging library)

Evidence:

  • No structured logging library found
  • Checked files: pyproject.toml
  • Using built-in logging module (unstructured)
📝 Remediation Steps

Add structured logging library for machine-parseable logs

  1. Choose structured logging library (structlog for Python, winston for Node.js)
  2. Install library and configure JSON formatter
  3. Add standard fields: timestamp, level, message, context
  4. Include request context: request_id, user_id, session_id
  5. Use consistent field naming (snake_case for Python)
  6. Never log sensitive data (passwords, tokens, PII)
  7. Configure different formats for dev (pretty) and prod (JSON)

Commands:

# Install structlog
pip install structlog

# Configure structlog
# See examples for configuration

Examples:

# Python with structlog
import structlog

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Good: Structured logging
logger.info(
    "user_login",
    user_id="123",
    email="user@example.com",
    ip_address="192.168.1.1"
)

# Bad: Unstructured logging
logger.info(f"User {user_id} logged in from {ip}")

Context Window Optimization

Attribute Tier Status Score
CLAUDE.md Configuration Files T1 ✅ pass 100
File Size Limits T2 ⊘ not_applicable —

Dependency Management

Attribute Tier Status Score
Lock Files for Reproducibility T1 ❌ fail 0
Dependency Freshness & Security T2 ⊘ not_applicable —

❌ Lock Files for Reproducibility

Measured: none (Threshold: at least one lock file)

Evidence:

  • No lock files found
📝 Remediation Steps

Add lock file for dependency reproducibility

  1. Use npm install, poetry lock, or equivalent to generate lock file

Commands:

npm install  # generates package-lock.json

Documentation

Attribute Tier Status Score
Concise Documentation T2 ❌ fail 70
Inline Documentation T2 ✅ pass 100

❌ Concise Documentation

Measured: 276 lines, 40 headings, 38 bullets (Threshold: <500 lines, structured format)

Evidence:

  • README length: 276 lines (excellent)
  • Heading density: 14.5 per 100 lines (target: 3-5)
  • 1 paragraphs exceed 10 lines (walls of text)
📝 Remediation Steps

Make documentation more concise and structured

  1. Break long README into multiple documents (docs/ directory)
  2. Add clear Markdown headings (##, ###) for structure
  3. Convert prose paragraphs to bullet points where possible
  4. Add table of contents for documents >100 lines
  5. Use code blocks instead of describing commands in prose
  6. Move detailed content to wiki or docs/, keep README focused

Commands:

# Check README length
wc -l README.md

# Count headings
grep -c '^#' README.md

Examples:

# Good: Concise with structure

## Quick Start
```bash
pip install -e .
agentready assess .

Features

  • Fast repository scanning
  • HTML and Markdown reports
  • 25 agent-ready attributes

Documentation

See docs/ for detailed guides.

Bad: Verbose prose

This project is a tool that helps you assess your repository
against best practices for AI-assisted development. It works by
scanning your codebase and checking for various attributes that
make repositories more effective when working with AI coding
assistants like Claude Code...

[Many more paragraphs of prose...]


</details>

### Documentation Standards

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| README Structure | T1 | ✅ pass | 100 |
| Architecture Decision Records (ADRs) | T3 | ❌ fail | 0 |
| Architecture Decision Records | T3 | ⊘ not_applicable | — |

#### ❌ Architecture Decision Records (ADRs)

**Measured**: no ADR directory (Threshold: ADR directory with decisions)

**Evidence**:
- No ADR directory found (checked docs/adr/, .adr/, adr/, docs/decisions/)

<details><summary><strong>📝 Remediation Steps</strong></summary>


Create Architecture Decision Records (ADRs) directory and document key decisions

1. Create docs/adr/ directory in repository root
2. Use Michael Nygard ADR template or MADR format
3. Document each significant architectural decision
4. Number ADRs sequentially (0001-*.md, 0002-*.md)
5. Include Status, Context, Decision, and Consequences sections
6. Update ADR status when decisions are revised (Superseded, Deprecated)

**Commands**:

```bash
# Create ADR directory
mkdir -p docs/adr

# Create first ADR using template
cat > docs/adr/0001-use-architecture-decision-records.md << 'EOF'
# 1. Use Architecture Decision Records

Date: 2025-11-22

## Status
Accepted

## Context
We need to record architectural decisions made in this project.

## Decision
We will use Architecture Decision Records (ADRs) as described by Michael Nygard.

## Consequences
- Decisions are documented with context
- Future contributors understand rationale
- ADRs are lightweight and version-controlled
EOF

Examples:

# Example ADR Structure

```markdown
# 2. Use PostgreSQL for Database

Date: 2025-11-22

## Status
Accepted

## Context
We need a relational database for complex queries and ACID transactions.
Team has PostgreSQL experience. Need full-text search capabilities.

## Decision
Use PostgreSQL 15+ as primary database.

## Consequences
- Positive: Robust ACID, full-text search, team familiarity
- Negative: Higher resource usage than SQLite
- Neutral: Need to manage migrations, backups


</details>

### Git & Version Control

| Attribute | Tier | Status | Score |
|-----------|------|--------|-------|
| Conventional Commit Messages | T2 | ❌ fail | 0 |
| .gitignore Completeness | T2 | ✅ pass | 100 |
| Branch Protection Rules | T4 | ⊘ not_applicable | — |
| Issue & Pull Request Templates | T4 | ⊘ not_applicable | — |

#### ❌ Conventional Commit Messages

**Measured**: not configured (Threshold: configured)

**Evidence**:
- No commitlint or husky configuration

<details><summary><strong>📝 Remediation Steps</strong></summary>


Configure conventional commits with commitlint

1. Install commitlint
2. Configure husky for commit-msg hook

**Commands**:

```bash
npm install --save-dev @commitlint/cli @commitlint/config-conventional husky

Performance

Attribute Tier Status Score
Performance Benchmarks T4 ⊘ not_applicable —

Repository Structure

Attribute Tier Status Score
Standard Project Layouts T1 ✅ pass 100
Issue & Pull Request Templates T3 ✅ pass 100
Separation of Concerns T2 ⊘ not_applicable —

Security

Attribute Tier Status Score
Security Scanning Automation T4 ⊘ not_applicable —

Testing & CI/CD

Attribute Tier Status Score
Test Coverage Requirements T2 ✅ pass 100
Pre-commit Hooks & CI/CD Linting T2 ✅ pass 100
CI/CD Pipeline Visibility T3 ✅ pass 80

🎯 Next Steps

Priority Improvements (highest impact first):

  1. Lock Files for Reproducibility (Tier 1) - +10.0 points potential
    • Add lock file for dependency reproducibility
  2. Type Annotations (Tier 1) - +10.0 points potential
    • Add type annotations to function signatures
  3. Conventional Commit Messages (Tier 2) - +3.0 points potential
    • Configure conventional commits with commitlint
  4. Concise Documentation (Tier 2) - +3.0 points potential
    • Make documentation more concise and structured
  5. Architecture Decision Records (ADRs) (Tier 3) - +1.5 points potential
    • Create Architecture Decision Records (ADRs) directory and document key decisions

📝 Assessment Metadata

  • Tool Version: AgentReady v1.0.0
  • Research Report: Bundled version
  • Repository Snapshot: 2121329
  • Assessment Duration: 1.3s

🤖 Generated with Claude Code

@jeremyeder jeremyeder merged commit 7762b23 into main Nov 24, 2025
6 of 7 checks passed
github-actions bot pushed a commit that referenced this pull request Nov 24, 2025
@semantic-release-bot
chore(release): 2.5.0 [skip ci]
e9557e4 
# [2.5.0](v2.4.0...v2.5.0) (2025-11-24)

### Features

* Batch Report Enhancements + Bootstrap Template Inheritance (Phase 2 Task 5) ([#133](#133)) ([7762b23](7762b23))
@github-actions
Copy link
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github-actions bot commented Nov 24, 2025

🎉 This PR is included in version 2.5.0 🎉

The release is available on GitHub release

Your semantic-release bot 📦🚀

github-actions bot pushed a commit to chambridge/agentready that referenced this pull request Jan 14, 2026
@semantic-release-bot
chore(release): 1.0.0 [skip ci]
4e08148 
# 1.0.0 (2026-01-14)

### Bug Fixes

* add bounded retry logic for LLM rate limit handling ([ambient-code#205](https://github.com/chambridge/agentready/issues/205)) ([6ecb786](6ecb786)), closes [ambient-code#104](https://github.com/chambridge/agentready/issues/104)
* Add comprehensive subprocess security guardrails (fixes [ambient-code#57](https://github.com/chambridge/agentready/issues/57)) ([ambient-code#66](https://github.com/chambridge/agentready/issues/66)) ([454b80e](454b80e))
* Add comprehensive YAML validation to prevent attacks (fixes [ambient-code#56](https://github.com/chambridge/agentready/issues/56)) ([ambient-code#63](https://github.com/chambridge/agentready/issues/63)) ([31ecb3a](31ecb3a))
* add repository checkout step to Claude Code Action workflow ([17aa0cf](17aa0cf))
* add uv.lock to recognized lockfiles ([ambient-code#143](https://github.com/chambridge/agentready/issues/143)) ([a98dc87](a98dc87)), closes [ambient-code#137](https://github.com/chambridge/agentready/issues/137)
* address P1 code quality issues from code review ([ambient-code#36](https://github.com/chambridge/agentready/issues/36)) ([5976332](5976332))
* address P1 code quality issues from code review ([ambient-code#37](https://github.com/chambridge/agentready/issues/37)) ([4be1d5e](4be1d5e))
* address P1 code quality issues from code review ([ambient-code#38](https://github.com/chambridge/agentready/issues/38)) ([77f2300](77f2300))
* **assessors:** search recursively for OpenAPI specification files ([ambient-code#127](https://github.com/chambridge/agentready/issues/127)) ([e2a5778](e2a5778))
* correct Assessment field name in demo command ([ambient-code#41](https://github.com/chambridge/agentready/issues/41)) ([b48622d](b48622d)), closes [ambient-code#12](https://github.com/chambridge/agentready/issues/12)
* Correct datetime import pattern in RepomixService ([ambient-code#65](https://github.com/chambridge/agentready/issues/65)) ([517aa6e](517aa6e))
* correct GitHub repository link in site navigation ([5492278](5492278))
* correct Liquid syntax in developer-guide (elif -> elsif) ([75f3b1d](75f3b1d))
* Create shared test fixtures and fix Assessment schema issues ([ambient-code#114](https://github.com/chambridge/agentready/issues/114)) ([46baa13](46baa13))
* disable attestations for Test PyPI to avoid conflict ([ambient-code#155](https://github.com/chambridge/agentready/issues/155)) ([a33e3cd](a33e3cd)), closes [pypa/#action-pypi-publish](https://github.com/chambridge/agentready/issues/action-pypi-publish)
* downgrade docker/metadata-action to v5 and fix shellcheck warnings ([12f5509](12f5509))
* enable Harbor task filtering for smoketest support ([ambient-code#222](https://github.com/chambridge/agentready/issues/222)) ([f780188](f780188))
* exclude DEPLOYMENT.md and SETUP_SUMMARY.md from Jekyll build ([9611207](9611207))
* Improve report metadata display with clean table format ([ca361a4](ca361a4))
* leaderboard workflow and SSH URL support ([ambient-code#147](https://github.com/chambridge/agentready/issues/147)) ([de28cd0](de28cd0))
* make E2E test timeouts configurable and add sensitive directory test ([ambient-code#206](https://github.com/chambridge/agentready/issues/206)) ([27e87e5](27e87e5)), closes [ambient-code#104](https://github.com/chambridge/agentready/issues/104) [ambient-code#192](https://github.com/chambridge/agentready/issues/192)
* P0 security and logic bugs from code review ([2af2346](2af2346))
* Prevent API key exposure in environment and logs (fixes [ambient-code#55](https://github.com/chambridge/agentready/issues/55)) ([ambient-code#64](https://github.com/chambridge/agentready/issues/64)) ([4d1d001](4d1d001))
* Prevent command injection in CommandFix.apply() (fixes [ambient-code#52](https://github.com/chambridge/agentready/issues/52)) ([ambient-code#60](https://github.com/chambridge/agentready/issues/60)) ([49be28e](49be28e))
* Prevent path traversal in LLM cache (fixes [ambient-code#53](https://github.com/chambridge/agentready/issues/53)) ([ambient-code#61](https://github.com/chambridge/agentready/issues/61)) ([2bf052d](2bf052d))
* Prevent XSS in HTML reports (fixes [ambient-code#54](https://github.com/chambridge/agentready/issues/54)) ([ambient-code#62](https://github.com/chambridge/agentready/issues/62)) ([7c60c69](7c60c69))
* rename research report in data directory ([b8ddfdc](b8ddfdc))
* replace all remaining elif with elsif in developer-guide ([73f16fc](73f16fc))
* Resolve 35 pytest failures through model validation and path sanitization improvements ([ambient-code#115](https://github.com/chambridge/agentready/issues/115)) ([4fbfee0](4fbfee0))
* resolve all test suite failures - achieve zero failures ([ambient-code#180](https://github.com/chambridge/agentready/issues/180)) ([990fa2d](990fa2d)), closes [ambient-code#148](https://github.com/chambridge/agentready/issues/148) [ambient-code#147](https://github.com/chambridge/agentready/issues/147) [ambient-code#145](https://github.com/chambridge/agentready/issues/145)
* resolve broken links and workflow failures ([ambient-code#160](https://github.com/chambridge/agentready/issues/160)) ([fbf5cf7](fbf5cf7))
* Resolve merge conflicts in CLI main module ([ambient-code#59](https://github.com/chambridge/agentready/issues/59)) ([9e0bf2d](9e0bf2d))
* resolve YAML syntax error in continuous-learning workflow ([ambient-code#172](https://github.com/chambridge/agentready/issues/172)) ([3d40fcc](3d40fcc))
* resolve YAML syntax error in update-docs workflow and add actionlint ([ambient-code#173](https://github.com/chambridge/agentready/issues/173)) ([97b06af](97b06af))
* Sanitize sensitive data in HTML reports (fixes [ambient-code#58](https://github.com/chambridge/agentready/issues/58)) ([ambient-code#67](https://github.com/chambridge/agentready/issues/67)) ([6fbac76](6fbac76))
* set correct baseurl for GitHub Pages subdirectory deployment ([c4db765](c4db765))
* skip PR comments for external forks to prevent permission errors ([ambient-code#163](https://github.com/chambridge/agentready/issues/163)) ([2a29fb8](2a29fb8))
* update --version flag to show correct version and research report date ([ambient-code#221](https://github.com/chambridge/agentready/issues/221)) ([5a85abb](5a85abb))
* Update Claude workflow to trigger on [@claude](https://github.com/claude) mentions ([ambient-code#35](https://github.com/chambridge/agentready/issues/35)) ([a8a3fab](a8a3fab))
* **workflows:** ensure post-comment step runs after Claude Code Action ([b087e5c](b087e5c))
* **workflows:** handle all event types in agentready-dev workflow ([9b942bf](9b942bf))
* **workflows:** improve error handling and logging for comment posting ([9ea1e6b](9ea1e6b))
* **workflows:** improve issue number extraction and add debug step ([ecd896b](ecd896b))
* **workflows:** remove if:always() to test step execution ([ff0bb12](ff0bb12))
* **workflows:** simplify post-comment step condition ([1bbf40a](1bbf40a))

### Features

* add agentready-dev Claude agent specification ([ambient-code#44](https://github.com/chambridge/agentready/issues/44)) ([0f61f5c](0f61f5c))
* add ambient-code/agentready to leaderboard ([ambient-code#148](https://github.com/chambridge/agentready/issues/148)) ([621152e](621152e))
* Add automated demo command for AgentReady ([ambient-code#24](https://github.com/chambridge/agentready/issues/24)) ([f4e89d9](f4e89d9)), closes [ambient-code#1](https://github.com/chambridge/agentready/issues/1) [ambient-code#25](https://github.com/chambridge/agentready/issues/25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring)
* add Claude Code GitHub Action for [@claude](https://github.com/claude) mentions ([3e7224d](3e7224d))
* Add comprehensive unit tests for utility modules (privacy.py and subprocess_utils.py) ([ambient-code#111](https://github.com/chambridge/agentready/issues/111)) ([9d3dece](9d3dece))
* Add customizable HTML report themes with runtime switching ([ambient-code#46](https://github.com/chambridge/agentready/issues/46)) ([7eeaf84](7eeaf84)), closes [hi#contrast](https://github.com/hi/issues/contrast) [ambient-code#10](https://github.com/chambridge/agentready/issues/10)
* Add Doubleagent - specialized AgentReady development agent ([ambient-code#30](https://github.com/chambridge/agentready/issues/30)) ([0ab54cb](0ab54cb))
* add GitHub organization scanning to assess-batch command ([ambient-code#118](https://github.com/chambridge/agentready/issues/118)) ([e306314](e306314))
* add Harbor Terminal-Bench comparison for agent effectiveness ([ambient-code#199](https://github.com/chambridge/agentready/issues/199)) ([a56e318](a56e318))
* Add Interactive Dashboard backlog item ([adfc4c8](adfc4c8))
* add interactive heatmap visualization for batch assessments ([ambient-code#136](https://github.com/chambridge/agentready/issues/136)) ([4d44fc3](4d44fc3))
* Add interactive HTML report generation ([18664ea](18664ea))
* add Memory MCP server allow list to repository settings ([ambient-code#203](https://github.com/chambridge/agentready/issues/203)) ([41d87bb](41d87bb))
* add quay/quay to leaderboard ([ambient-code#162](https://github.com/chambridge/agentready/issues/162)) ([d6e8df0](d6e8df0))
* add release pipeline coldstart prompt ([ambient-code#19](https://github.com/chambridge/agentready/issues/19)) ([9a3880c](9a3880c)), closes [ambient-code#18](https://github.com/chambridge/agentready/issues/18)
* Add Repomix integration for AI-friendly repository context generation ([ambient-code#29](https://github.com/chambridge/agentready/issues/29)) ([92bdde1](92bdde1)), closes [ambient-code#24](https://github.com/chambridge/agentready/issues/24) [ambient-code#1](https://github.com/chambridge/agentready/issues/1) [ambient-code#25](https://github.com/chambridge/agentready/issues/25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring)
* add report header with repository metadata ([ambient-code#28](https://github.com/chambridge/agentready/issues/28)) ([7a8b34a](7a8b34a))
* Add research report management CLI commands ([ambient-code#45](https://github.com/chambridge/agentready/issues/45)) ([e1be488](e1be488)), closes [ambient-code#7](https://github.com/chambridge/agentready/issues/7)
* Add security & quality improvements from code review ([ambient-code#40](https://github.com/chambridge/agentready/issues/40)) ([13cd3ca](13cd3ca))
* Add security & quality improvements from code review ([ambient-code#49](https://github.com/chambridge/agentready/issues/49)) ([889d6ed](889d6ed))
* Add SWE-bench experiment system for validating AgentReady impact ([ambient-code#124](https://github.com/chambridge/agentready/issues/124)) ([15edbba](15edbba))
* Add weekly research update skill and automation ([ambient-code#145](https://github.com/chambridge/agentready/issues/145)) ([7ba17a6](7ba17a6))
* **assessors:** implement File Size Limits assessor (Tier 2) ([ambient-code#141](https://github.com/chambridge/agentready/issues/141)) ([248467f](248467f))
* Auto-sync CLAUDE.md during semantic-release ([ambient-code#101](https://github.com/chambridge/agentready/issues/101)) ([36b48cb](36b48cb))
* automate PyPI publishing with trusted publishing (OIDC) ([ambient-code#154](https://github.com/chambridge/agentready/issues/154)) ([71f4632](71f4632)), closes [pypa/#action-pypi-publish](https://github.com/chambridge/agentready/issues/action-pypi-publish)
* Batch Report Enhancements + Bootstrap Template Inheritance (Phase 2 Task 5) ([ambient-code#133](https://github.com/chambridge/agentready/issues/133)) ([7762b23](7762b23))
* Community Leaderboard for AgentReady Scores ([ambient-code#146](https://github.com/chambridge/agentready/issues/146)) ([fea0b3e](fea0b3e))
* Complete Phases 5-7 - Markdown reports, testing, and polish ([7659623](7659623))
* consolidate GitHub Actions workflows by purpose ([ambient-code#217](https://github.com/chambridge/agentready/issues/217)) ([717ca6b](717ca6b)), closes [ambient-code#221](https://github.com/chambridge/agentready/issues/221)
* container support ([ambient-code#171](https://github.com/chambridge/agentready/issues/171)) ([c6874ea](c6874ea))
* convert AgentReady assessment to on-demand workflow ([ambient-code#213](https://github.com/chambridge/agentready/issues/213)) ([b5a1ce0](b5a1ce0)), closes [ambient-code#191](https://github.com/chambridge/agentready/issues/191)
* enhance assessors with multi-language support and security ([ambient-code#200](https://github.com/chambridge/agentready/issues/200)) ([85712f2](85712f2)), closes [ambient-code#10](https://github.com/chambridge/agentready/issues/10)
* Harbor framework integration for Terminal-Bench evaluations ([ambient-code#202](https://github.com/chambridge/agentready/issues/202)) ([d73a8c8](d73a8c8)), closes [ambient-code#4](https://github.com/chambridge/agentready/issues/4) [ambient-code#178](https://github.com/chambridge/agentready/issues/178) [ambient-code#178](https://github.com/chambridge/agentready/issues/178)
* Implement AgentReady MVP with scoring engine ([54a96cb](54a96cb))
* Implement align subcommand for automated remediation (Issue [ambient-code#14](https://github.com/chambridge/agentready/issues/14)) ([ambient-code#34](https://github.com/chambridge/agentready/issues/34)) ([06f04dc](06f04dc))
* Implement ArchitectureDecisionsAssessor (fixes [ambient-code#81](https://github.com/chambridge/agentready/issues/81)) ([ambient-code#89](https://github.com/chambridge/agentready/issues/89)) ([9e782e5](9e782e5))
* implement automated semantic release pipeline ([ambient-code#20](https://github.com/chambridge/agentready/issues/20)) ([b579235](b579235))
* implement bootstrap command for GitHub infrastructure ([0af06c4](0af06c4)), closes [ambient-code#2](https://github.com/chambridge/agentready/issues/2)
* Implement BranchProtectionAssessor stub (fixes [ambient-code#86](https://github.com/chambridge/agentready/issues/86)) ([ambient-code#98](https://github.com/chambridge/agentready/issues/98)) ([44c4b17](44c4b17))
* Implement CICDPipelineVisibilityAssessor (fixes [ambient-code#85](https://github.com/chambridge/agentready/issues/85)) ([ambient-code#91](https://github.com/chambridge/agentready/issues/91)) ([e68285c](e68285c))
* Implement CodeSmellsAssessor stub (fixes [ambient-code#87](https://github.com/chambridge/agentready/issues/87)) ([ambient-code#99](https://github.com/chambridge/agentready/issues/99)) ([f06b2a8](f06b2a8))
* Implement ConciseDocumentationAssessor (fixes [ambient-code#76](https://github.com/chambridge/agentready/issues/76)) ([ambient-code#93](https://github.com/chambridge/agentready/issues/93)) ([c356cd5](c356cd5))
* Implement InlineDocumentationAssessor (fixes [ambient-code#77](https://github.com/chambridge/agentready/issues/77)) ([ambient-code#94](https://github.com/chambridge/agentready/issues/94)) ([e56e570](e56e570))
* Implement IssuePRTemplatesAssessor (fixes [ambient-code#84](https://github.com/chambridge/agentready/issues/84)) ([ambient-code#90](https://github.com/chambridge/agentready/issues/90)) ([819d7b7](819d7b7))
* Implement multi-repository batch assessment (Phase 1 of issue [ambient-code#68](https://github.com/chambridge/agentready/issues/68)) ([ambient-code#74](https://github.com/chambridge/agentready/issues/74)) ([befc0d5](befc0d5))
* Implement OneCommandSetupAssessor (fixes [ambient-code#75](https://github.com/chambridge/agentready/issues/75)) ([ambient-code#88](https://github.com/chambridge/agentready/issues/88)) ([668ba1b](668ba1b))
* Implement OpenAPISpecsAssessor (fixes [ambient-code#80](https://github.com/chambridge/agentready/issues/80)) ([ambient-code#97](https://github.com/chambridge/agentready/issues/97)) ([45ae36e](45ae36e))
* implement Phase 2 multi-repository assessment reporting ([ambient-code#117](https://github.com/chambridge/agentready/issues/117)) ([8da56c2](8da56c2)), closes [ambient-code#69](https://github.com/chambridge/agentready/issues/69)
* implement report schema versioning ([ambient-code#43](https://github.com/chambridge/agentready/issues/43)) ([4c4752c](4c4752c))
* Implement SemanticNamingAssessor (fixes [ambient-code#82](https://github.com/chambridge/agentready/issues/82)) ([ambient-code#95](https://github.com/chambridge/agentready/issues/95)) ([d87a280](d87a280))
* Implement SeparationOfConcernsAssessor (fixes [ambient-code#78](https://github.com/chambridge/agentready/issues/78)) ([ambient-code#92](https://github.com/chambridge/agentready/issues/92)) ([99bfe28](99bfe28))
* Implement StructuredLoggingAssessor (fixes [ambient-code#79](https://github.com/chambridge/agentready/issues/79)) ([ambient-code#96](https://github.com/chambridge/agentready/issues/96)) ([2b87ca7](2b87ca7))
* Phase 1 Task 1 - Consolidate Security Validation Patterns ([ambient-code#129](https://github.com/chambridge/agentready/issues/129)) ([8580c45](8580c45)), closes [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122)
* Phase 1 Tasks 2-3 - Consolidate Reporter Base & Assessor Factory ([ambient-code#131](https://github.com/chambridge/agentready/issues/131)) ([8e12bf9](8e12bf9)), closes [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122)
* Phase 2 Task 4 - Replace manual config validation with Pydantic ([ambient-code#134](https://github.com/chambridge/agentready/issues/134)) ([d83cf58](d83cf58))
* Redesign homepage features with two-column layout and research links ([ambient-code#189](https://github.com/chambridge/agentready/issues/189)) ([570087d](570087d)), closes [ambient-code#187](https://github.com/chambridge/agentready/issues/187)
* redesign HTML report with dark theme and larger fonts ([ambient-code#39](https://github.com/chambridge/agentready/issues/39)) ([59f6702](59f6702)), closes [#8b5cf6](https://github.com/chambridge/agentready/issues/8b5cf6) [#XX](https://github.com/chambridge/agentready/issues/XX)
* Rename 'learn' command to 'extract-skills' for clarity ([ambient-code#125](https://github.com/chambridge/agentready/issues/125)) ([64d6563](64d6563)), closes [hi#scoring](https://github.com/hi/issues/scoring) [ambient-code#123](https://github.com/chambridge/agentready/issues/123)
* replace markdown-link-check with lychee for link validation ([ambient-code#177](https://github.com/chambridge/agentready/issues/177)) ([f1a4545](f1a4545))
* Standardize on Python 3.12+ with forward compatibility for 3.13 ([ambient-code#132](https://github.com/chambridge/agentready/issues/132)) ([84f2c46](84f2c46))
* Terminal-Bench eval harness (MVP Phase 1) ([ambient-code#178](https://github.com/chambridge/agentready/issues/178)) ([d06bab4](d06bab4)), closes [ambient-code#171](https://github.com/chambridge/agentready/issues/171)
* **workflows:** add comment posting for [@agentready-dev](https://github.com/agentready-dev) agent ([5dff614](5dff614))

### Performance Improvements

* implement lazy loading for heavy CLI commands ([ambient-code#151](https://github.com/chambridge/agentready/issues/151)) ([6a7cd4e](6a7cd4e))

### BREAKING CHANGES

* Users must update scripts from 'agentready learn'
to 'agentready extract-skills'. All flags and options remain identical.
github-actions bot pushed a commit to chambridge/agentready that referenced this pull request Jan 16, 2026
@semantic-release-bot
chore(release): 1.0.0 [skip ci]
f929b84 
# 1.0.0 (2026-01-16)

### Bug Fixes

* add bounded retry logic for LLM rate limit handling ([ambient-code#205](https://github.com/chambridge/agentready/issues/205)) ([6ecb786](6ecb786)), closes [ambient-code#104](https://github.com/chambridge/agentready/issues/104)
* Add comprehensive subprocess security guardrails (fixes [ambient-code#57](https://github.com/chambridge/agentready/issues/57)) ([ambient-code#66](https://github.com/chambridge/agentready/issues/66)) ([454b80e](454b80e))
* Add comprehensive YAML validation to prevent attacks (fixes [ambient-code#56](https://github.com/chambridge/agentready/issues/56)) ([ambient-code#63](https://github.com/chambridge/agentready/issues/63)) ([31ecb3a](31ecb3a))
* add repository checkout step to Claude Code Action workflow ([17aa0cf](17aa0cf))
* add uv.lock to recognized lockfiles ([ambient-code#143](https://github.com/chambridge/agentready/issues/143)) ([a98dc87](a98dc87)), closes [ambient-code#137](https://github.com/chambridge/agentready/issues/137)
* address P1 code quality issues from code review ([ambient-code#36](https://github.com/chambridge/agentready/issues/36)) ([5976332](5976332))
* address P1 code quality issues from code review ([ambient-code#37](https://github.com/chambridge/agentready/issues/37)) ([4be1d5e](4be1d5e))
* address P1 code quality issues from code review ([ambient-code#38](https://github.com/chambridge/agentready/issues/38)) ([77f2300](77f2300))
* **assessors:** FileSizeLimitsAssessor now respects .gitignore ([ambient-code#248](https://github.com/chambridge/agentready/issues/248)) ([eaaecc2](eaaecc2)), closes [ambient-code#245](https://github.com/chambridge/agentready/issues/245)
* **assessors:** search recursively for OpenAPI specification files ([ambient-code#127](https://github.com/chambridge/agentready/issues/127)) ([e2a5778](e2a5778))
* **ci:** use gh pr view for fork PR number lookup in coverage comment ([ambient-code#253](https://github.com/chambridge/agentready/issues/253)) ([1688362](1688362))
* correct Assessment field name in demo command ([ambient-code#41](https://github.com/chambridge/agentready/issues/41)) ([b48622d](b48622d)), closes [ambient-code#12](https://github.com/chambridge/agentready/issues/12)
* Correct datetime import pattern in RepomixService ([ambient-code#65](https://github.com/chambridge/agentready/issues/65)) ([517aa6e](517aa6e))
* correct GitHub repository link in site navigation ([5492278](5492278))
* correct Liquid syntax in developer-guide (elif -> elsif) ([75f3b1d](75f3b1d))
* Create shared test fixtures and fix Assessment schema issues ([ambient-code#114](https://github.com/chambridge/agentready/issues/114)) ([46baa13](46baa13))
* disable attestations for Test PyPI to avoid conflict ([ambient-code#155](https://github.com/chambridge/agentready/issues/155)) ([a33e3cd](a33e3cd)), closes [pypa/#action-pypi-publish](https://github.com/chambridge/agentready/issues/action-pypi-publish)
* downgrade docker/metadata-action to v5 and fix shellcheck warnings ([12f5509](12f5509))
* enable Harbor task filtering for smoketest support ([ambient-code#222](https://github.com/chambridge/agentready/issues/222)) ([f780188](f780188))
* exclude DEPLOYMENT.md and SETUP_SUMMARY.md from Jekyll build ([9611207](9611207))
* Improve report metadata display with clean table format ([ca361a4](ca361a4))
* leaderboard workflow and SSH URL support ([ambient-code#147](https://github.com/chambridge/agentready/issues/147)) ([de28cd0](de28cd0))
* make E2E test timeouts configurable and add sensitive directory test ([ambient-code#206](https://github.com/chambridge/agentready/issues/206)) ([27e87e5](27e87e5)), closes [ambient-code#104](https://github.com/chambridge/agentready/issues/104) [ambient-code#192](https://github.com/chambridge/agentready/issues/192)
* P0 security and logic bugs from code review ([2af2346](2af2346))
* Prevent API key exposure in environment and logs (fixes [ambient-code#55](https://github.com/chambridge/agentready/issues/55)) ([ambient-code#64](https://github.com/chambridge/agentready/issues/64)) ([4d1d001](4d1d001))
* Prevent command injection in CommandFix.apply() (fixes [ambient-code#52](https://github.com/chambridge/agentready/issues/52)) ([ambient-code#60](https://github.com/chambridge/agentready/issues/60)) ([49be28e](49be28e))
* Prevent path traversal in LLM cache (fixes [ambient-code#53](https://github.com/chambridge/agentready/issues/53)) ([ambient-code#61](https://github.com/chambridge/agentready/issues/61)) ([2bf052d](2bf052d))
* prevent unauthorized message for non-command comments ([ambient-code#262](https://github.com/chambridge/agentready/issues/262)) ([84c6f69](84c6f69))
* Prevent XSS in HTML reports (fixes [ambient-code#54](https://github.com/chambridge/agentready/issues/54)) ([ambient-code#62](https://github.com/chambridge/agentready/issues/62)) ([7c60c69](7c60c69))
* rename research report in data directory ([b8ddfdc](b8ddfdc))
* replace all remaining elif with elsif in developer-guide ([73f16fc](73f16fc))
* Resolve 35 pytest failures through model validation and path sanitization improvements ([ambient-code#115](https://github.com/chambridge/agentready/issues/115)) ([4fbfee0](4fbfee0))
* resolve all test suite failures - achieve zero failures ([ambient-code#180](https://github.com/chambridge/agentready/issues/180)) ([990fa2d](990fa2d)), closes [ambient-code#148](https://github.com/chambridge/agentready/issues/148) [ambient-code#147](https://github.com/chambridge/agentready/issues/147) [ambient-code#145](https://github.com/chambridge/agentready/issues/145)
* resolve broken links and workflow failures ([ambient-code#160](https://github.com/chambridge/agentready/issues/160)) ([fbf5cf7](fbf5cf7))
* Resolve merge conflicts in CLI main module ([ambient-code#59](https://github.com/chambridge/agentready/issues/59)) ([9e0bf2d](9e0bf2d))
* resolve YAML syntax error in continuous-learning workflow ([ambient-code#172](https://github.com/chambridge/agentready/issues/172)) ([3d40fcc](3d40fcc))
* resolve YAML syntax error in update-docs workflow and add actionlint ([ambient-code#173](https://github.com/chambridge/agentready/issues/173)) ([97b06af](97b06af))
* Sanitize sensitive data in HTML reports (fixes [ambient-code#58](https://github.com/chambridge/agentready/issues/58)) ([ambient-code#67](https://github.com/chambridge/agentready/issues/67)) ([6fbac76](6fbac76))
* set correct baseurl for GitHub Pages subdirectory deployment ([c4db765](c4db765))
* skip PR comments for external forks to prevent permission errors ([ambient-code#163](https://github.com/chambridge/agentready/issues/163)) ([2a29fb8](2a29fb8))
* update --version flag to show correct version and research report date ([ambient-code#221](https://github.com/chambridge/agentready/issues/221)) ([5a85abb](5a85abb))
* Update Claude workflow to trigger on [@claude](https://github.com/claude) mentions ([ambient-code#35](https://github.com/chambridge/agentready/issues/35)) ([a8a3fab](a8a3fab))
* **workflows:** ensure post-comment step runs after Claude Code Action ([b087e5c](b087e5c))
* **workflows:** handle all event types in agentready-dev workflow ([9b942bf](9b942bf))
* **workflows:** improve error handling and logging for comment posting ([9ea1e6b](9ea1e6b))
* **workflows:** improve issue number extraction and add debug step ([ecd896b](ecd896b))
* **workflows:** remove if:always() to test step execution ([ff0bb12](ff0bb12))
* **workflows:** simplify post-comment step condition ([1bbf40a](1bbf40a))

### Features

* add agentready-dev Claude agent specification ([ambient-code#44](https://github.com/chambridge/agentready/issues/44)) ([0f61f5c](0f61f5c))
* add ambient-code/agentready to leaderboard ([ambient-code#148](https://github.com/chambridge/agentready/issues/148)) ([621152e](621152e))
* Add automated demo command for AgentReady ([ambient-code#24](https://github.com/chambridge/agentready/issues/24)) ([f4e89d9](f4e89d9)), closes [ambient-code#1](https://github.com/chambridge/agentready/issues/1) [ambient-code#25](https://github.com/chambridge/agentready/issues/25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring)
* add Claude Code GitHub Action for [@claude](https://github.com/claude) mentions ([3e7224d](3e7224d))
* Add comprehensive unit tests for utility modules (privacy.py and subprocess_utils.py) ([ambient-code#111](https://github.com/chambridge/agentready/issues/111)) ([9d3dece](9d3dece))
* Add customizable HTML report themes with runtime switching ([ambient-code#46](https://github.com/chambridge/agentready/issues/46)) ([7eeaf84](7eeaf84)), closes [hi#contrast](https://github.com/hi/issues/contrast) [ambient-code#10](https://github.com/chambridge/agentready/issues/10)
* Add Doubleagent - specialized AgentReady development agent ([ambient-code#30](https://github.com/chambridge/agentready/issues/30)) ([0ab54cb](0ab54cb))
* add GitHub organization scanning to assess-batch command ([ambient-code#118](https://github.com/chambridge/agentready/issues/118)) ([e306314](e306314))
* add Harbor Terminal-Bench comparison for agent effectiveness ([ambient-code#199](https://github.com/chambridge/agentready/issues/199)) ([a56e318](a56e318))
* Add Interactive Dashboard backlog item ([adfc4c8](adfc4c8))
* add interactive heatmap visualization for batch assessments ([ambient-code#136](https://github.com/chambridge/agentready/issues/136)) ([4d44fc3](4d44fc3))
* Add interactive HTML report generation ([18664ea](18664ea))
* add Memory MCP server allow list to repository settings ([ambient-code#203](https://github.com/chambridge/agentready/issues/203)) ([41d87bb](41d87bb))
* add quay/quay to leaderboard ([ambient-code#162](https://github.com/chambridge/agentready/issues/162)) ([d6e8df0](d6e8df0))
* add release pipeline coldstart prompt ([ambient-code#19](https://github.com/chambridge/agentready/issues/19)) ([9a3880c](9a3880c)), closes [ambient-code#18](https://github.com/chambridge/agentready/issues/18)
* Add Repomix integration for AI-friendly repository context generation ([ambient-code#29](https://github.com/chambridge/agentready/issues/29)) ([92bdde1](92bdde1)), closes [ambient-code#24](https://github.com/chambridge/agentready/issues/24) [ambient-code#1](https://github.com/chambridge/agentready/issues/1) [ambient-code#25](https://github.com/chambridge/agentready/issues/25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring)
* add report header with repository metadata ([ambient-code#28](https://github.com/chambridge/agentready/issues/28)) ([7a8b34a](7a8b34a))
* Add research report management CLI commands ([ambient-code#45](https://github.com/chambridge/agentready/issues/45)) ([e1be488](e1be488)), closes [ambient-code#7](https://github.com/chambridge/agentready/issues/7)
* Add security & quality improvements from code review ([ambient-code#40](https://github.com/chambridge/agentready/issues/40)) ([13cd3ca](13cd3ca))
* Add security & quality improvements from code review ([ambient-code#49](https://github.com/chambridge/agentready/issues/49)) ([889d6ed](889d6ed))
* Add SWE-bench experiment system for validating AgentReady impact ([ambient-code#124](https://github.com/chambridge/agentready/issues/124)) ([15edbba](15edbba))
* Add weekly research update skill and automation ([ambient-code#145](https://github.com/chambridge/agentready/issues/145)) ([7ba17a6](7ba17a6))
* **assessors:** implement File Size Limits assessor (Tier 2) ([ambient-code#141](https://github.com/chambridge/agentready/issues/141)) ([248467f](248467f))
* Auto-sync CLAUDE.md during semantic-release ([ambient-code#101](https://github.com/chambridge/agentready/issues/101)) ([36b48cb](36b48cb))
* automate PyPI publishing with trusted publishing (OIDC) ([ambient-code#154](https://github.com/chambridge/agentready/issues/154)) ([71f4632](71f4632)), closes [pypa/#action-pypi-publish](https://github.com/chambridge/agentready/issues/action-pypi-publish)
* Batch Report Enhancements + Bootstrap Template Inheritance (Phase 2 Task 5) ([ambient-code#133](https://github.com/chambridge/agentready/issues/133)) ([7762b23](7762b23))
* Community Leaderboard for AgentReady Scores ([ambient-code#146](https://github.com/chambridge/agentready/issues/146)) ([fea0b3e](fea0b3e))
* Complete Phases 5-7 - Markdown reports, testing, and polish ([7659623](7659623))
* consolidate GitHub Actions workflows by purpose ([ambient-code#217](https://github.com/chambridge/agentready/issues/217)) ([717ca6b](717ca6b)), closes [ambient-code#221](https://github.com/chambridge/agentready/issues/221)
* container support ([ambient-code#171](https://github.com/chambridge/agentready/issues/171)) ([c6874ea](c6874ea))
* convert AgentReady assessment to on-demand workflow ([ambient-code#213](https://github.com/chambridge/agentready/issues/213)) ([b5a1ce0](b5a1ce0)), closes [ambient-code#191](https://github.com/chambridge/agentready/issues/191)
* enhance assessors with multi-language support and security ([ambient-code#200](https://github.com/chambridge/agentready/issues/200)) ([85712f2](85712f2)), closes [ambient-code#10](https://github.com/chambridge/agentready/issues/10)
* Harbor framework integration for Terminal-Bench evaluations ([ambient-code#202](https://github.com/chambridge/agentready/issues/202)) ([d73a8c8](d73a8c8)), closes [ambient-code#4](https://github.com/chambridge/agentready/issues/4) [ambient-code#178](https://github.com/chambridge/agentready/issues/178) [ambient-code#178](https://github.com/chambridge/agentready/issues/178)
* Implement AgentReady MVP with scoring engine ([54a96cb](54a96cb))
* Implement align subcommand for automated remediation (Issue [ambient-code#14](https://github.com/chambridge/agentready/issues/14)) ([ambient-code#34](https://github.com/chambridge/agentready/issues/34)) ([06f04dc](06f04dc))
* Implement ArchitectureDecisionsAssessor (fixes [ambient-code#81](https://github.com/chambridge/agentready/issues/81)) ([ambient-code#89](https://github.com/chambridge/agentready/issues/89)) ([9e782e5](9e782e5))
* implement automated semantic release pipeline ([ambient-code#20](https://github.com/chambridge/agentready/issues/20)) ([b579235](b579235))
* implement bootstrap command for GitHub infrastructure ([0af06c4](0af06c4)), closes [ambient-code#2](https://github.com/chambridge/agentready/issues/2)
* Implement BranchProtectionAssessor stub (fixes [ambient-code#86](https://github.com/chambridge/agentready/issues/86)) ([ambient-code#98](https://github.com/chambridge/agentready/issues/98)) ([44c4b17](44c4b17))
* Implement CICDPipelineVisibilityAssessor (fixes [ambient-code#85](https://github.com/chambridge/agentready/issues/85)) ([ambient-code#91](https://github.com/chambridge/agentready/issues/91)) ([e68285c](e68285c))
* Implement CodeSmellsAssessor stub (fixes [ambient-code#87](https://github.com/chambridge/agentready/issues/87)) ([ambient-code#99](https://github.com/chambridge/agentready/issues/99)) ([f06b2a8](f06b2a8))
* Implement ConciseDocumentationAssessor (fixes [ambient-code#76](https://github.com/chambridge/agentready/issues/76)) ([ambient-code#93](https://github.com/chambridge/agentready/issues/93)) ([c356cd5](c356cd5))
* Implement InlineDocumentationAssessor (fixes [ambient-code#77](https://github.com/chambridge/agentready/issues/77)) ([ambient-code#94](https://github.com/chambridge/agentready/issues/94)) ([e56e570](e56e570))
* Implement IssuePRTemplatesAssessor (fixes [ambient-code#84](https://github.com/chambridge/agentready/issues/84)) ([ambient-code#90](https://github.com/chambridge/agentready/issues/90)) ([819d7b7](819d7b7))
* Implement multi-repository batch assessment (Phase 1 of issue [ambient-code#68](https://github.com/chambridge/agentready/issues/68)) ([ambient-code#74](https://github.com/chambridge/agentready/issues/74)) ([befc0d5](befc0d5))
* Implement OneCommandSetupAssessor (fixes [ambient-code#75](https://github.com/chambridge/agentready/issues/75)) ([ambient-code#88](https://github.com/chambridge/agentready/issues/88)) ([668ba1b](668ba1b))
* Implement OpenAPISpecsAssessor (fixes [ambient-code#80](https://github.com/chambridge/agentready/issues/80)) ([ambient-code#97](https://github.com/chambridge/agentready/issues/97)) ([45ae36e](45ae36e))
* implement Phase 2 multi-repository assessment reporting ([ambient-code#117](https://github.com/chambridge/agentready/issues/117)) ([8da56c2](8da56c2)), closes [ambient-code#69](https://github.com/chambridge/agentready/issues/69)
* implement report schema versioning ([ambient-code#43](https://github.com/chambridge/agentready/issues/43)) ([4c4752c](4c4752c))
* Implement SemanticNamingAssessor (fixes [ambient-code#82](https://github.com/chambridge/agentready/issues/82)) ([ambient-code#95](https://github.com/chambridge/agentready/issues/95)) ([d87a280](d87a280))
* Implement SeparationOfConcernsAssessor (fixes [ambient-code#78](https://github.com/chambridge/agentready/issues/78)) ([ambient-code#92](https://github.com/chambridge/agentready/issues/92)) ([99bfe28](99bfe28))
* Implement StructuredLoggingAssessor (fixes [ambient-code#79](https://github.com/chambridge/agentready/issues/79)) ([ambient-code#96](https://github.com/chambridge/agentready/issues/96)) ([2b87ca7](2b87ca7))
* Phase 1 Task 1 - Consolidate Security Validation Patterns ([ambient-code#129](https://github.com/chambridge/agentready/issues/129)) ([8580c45](8580c45)), closes [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122)
* Phase 1 Tasks 2-3 - Consolidate Reporter Base & Assessor Factory ([ambient-code#131](https://github.com/chambridge/agentready/issues/131)) ([8e12bf9](8e12bf9)), closes [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122) [ambient-code#122](https://github.com/chambridge/agentready/issues/122)
* Phase 2 Task 4 - Replace manual config validation with Pydantic ([ambient-code#134](https://github.com/chambridge/agentready/issues/134)) ([d83cf58](d83cf58))
* Redesign homepage features with two-column layout and research links ([ambient-code#189](https://github.com/chambridge/agentready/issues/189)) ([570087d](570087d)), closes [ambient-code#187](https://github.com/chambridge/agentready/issues/187)
* redesign HTML report with dark theme and larger fonts ([ambient-code#39](https://github.com/chambridge/agentready/issues/39)) ([59f6702](59f6702)), closes [#8b5cf6](https://github.com/chambridge/agentready/issues/8b5cf6) [#XX](https://github.com/chambridge/agentready/issues/XX)
* Rename 'learn' command to 'extract-skills' for clarity ([ambient-code#125](https://github.com/chambridge/agentready/issues/125)) ([64d6563](64d6563)), closes [hi#scoring](https://github.com/hi/issues/scoring) [ambient-code#123](https://github.com/chambridge/agentready/issues/123)
* replace markdown-link-check with lychee for link validation ([ambient-code#177](https://github.com/chambridge/agentready/issues/177)) ([f1a4545](f1a4545))
* Standardize on Python 3.12+ with forward compatibility for 3.13 ([ambient-code#132](https://github.com/chambridge/agentready/issues/132)) ([84f2c46](84f2c46))
* Terminal-Bench eval harness (MVP Phase 1) ([ambient-code#178](https://github.com/chambridge/agentready/issues/178)) ([d06bab4](d06bab4)), closes [ambient-code#171](https://github.com/chambridge/agentready/issues/171)
* **workflows:** add comment posting for [@agentready-dev](https://github.com/agentready-dev) agent ([5dff614](5dff614))

### Performance Improvements

* implement lazy loading for heavy CLI commands ([ambient-code#151](https://github.com/chambridge/agentready/issues/151)) ([6a7cd4e](6a7cd4e))

### BREAKING CHANGES

* Users must update scripts from 'agentready learn'
to 'agentready extract-skills'. All flags and options remain identical.
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@jeremyeder