feat: Add automated demo command for AgentReady #24

jeremyeder · 2025-11-21T10:11:56Z

Implements P0 feature to showcase AgentReady capabilities with a single command.

Features:

agentready demo command creates sample repository and runs assessment
Supports Python (default) and JavaScript demo repositories
Real-time progress indicators with color-coded output (✓/✗/⊘)
Auto-opens HTML report in browser (optional --no-browser flag)
Generates HTML, Markdown, and JSON reports in .agentready-demo/
Options: --language, --no-browser, --keep-repo

Implementation:

Created src/agentready/cli/demo.py with demo command logic
Registered demo command in CLI main.py
Added comprehensive unit tests in tests/unit/test_demo.py
Demo completes in ~3-5 seconds with 25 attribute assessments

Perfect for presentations, stakeholder demos, and user onboarding.

Closes #1

🤖 Generated with Claude Code

Description

Type of Change

Bug fix (non-breaking change which fixes an issue)
New feature (non-breaking change which adds functionality)
Breaking change (fix or feature that would cause existing functionality to not work as expected)
Documentation update
Refactoring (no functional changes)
Performance improvement
Test coverage improvement

Related Issues

Fixes #
Relates to #

Changes Made

Testing

Unit tests pass (pytest)
Integration tests pass
Manual testing performed
No new warnings or errors

Checklist

My code follows the project's code style
I have performed a self-review of my own code
I have commented my code, particularly in hard-to-understand areas
I have made corresponding changes to the documentation
My changes generate no new warnings
I have added tests that prove my fix is effective or that my feature works
New and existing unit tests pass locally with my changes
Any dependent changes have been merged and published

Screenshots (if applicable)

Additional Notes

Implements P0 feature to showcase AgentReady capabilities with a single command. Features: - agentready demo command creates sample repository and runs assessment - Supports Python (default) and JavaScript demo repositories - Real-time progress indicators with color-coded output (✓/✗/⊘) - Auto-opens HTML report in browser (optional --no-browser flag) - Generates HTML, Markdown, and JSON reports in .agentready-demo/ - Options: --language, --no-browser, --keep-repo Implementation: - Created src/agentready/cli/demo.py with demo command logic - Registered demo command in CLI main.py - Added comprehensive unit tests in tests/unit/test_demo.py - Demo completes in ~3-5 seconds with 25 attribute assessments Perfect for presentations, stakeholder demos, and user onboarding. Closes #1 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com>

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chatgpt-codex-connector · 2025-11-21T10:18:00Z

src/agentready/cli/demo.py

+    repo = git.Repo.init(demo_path)
+    repo.index.add(["*"])
+    repo.index.commit("Initial commit - Demo repository for AgentReady")


Demo git commit assumes preconfigured identity

The demo command initializes a git repo and immediately commits it without setting an author/committer. On machines where user.name/user.email are not already configured (e.g., fresh installs or CI containers), repo.index.commit(...) raises GitCommandError: Please tell me who you are, aborting the demo before any assessments run. For a “just works” demo flow, set an explicit actor or temporary repo config before committing so the command succeeds even on clean environments.

Useful? React with 👍 / 👎.

The workflow was attempting to install agentready from PyPI, but the package has never been published. Changed to install from checked-out repository source using 'pip install -e .' instead. This ensures CI tests the actual code in the PR rather than a potentially stale PyPI version. Fixes GitHub Actions workflow failure on PR #24. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Added workflow_dispatch workflow for manual PyPI publishing with: - Dry run mode (publishes to TestPyPI for testing) - Production mode (publishes to PyPI) - Optional version override - Automatic GitHub release creation - Built-in validation with twine check - Secure: Uses environment variables to prevent command injection Usage: 1. Configure TEST_PYPI_TOKEN and PYPI_TOKEN secrets 2. Go to Actions → "Publish to PyPI" → Run workflow 3. Choose dry run (TestPyPI) or production (PyPI) Future enhancement tracked in issue #25 for automated integration with semantic-release workflow. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

…ction Add comprehensive learning system that extracts high-quality skills from assessments: - New 'learn' CLI command with heuristic and LLM enrichment modes - Claude API integration for detailed skill analysis and instruction generation - LLM response caching system (7-day TTL) to reduce API costs - Code sampling for real repository examples - Pattern extraction from high-scoring assessment attributes - Support for multiple output formats (JSON, SKILL.md, GitHub issues) Technical additions: - src/agentready/cli/learn.py - Main learning command implementation - src/agentready/learners/ - Pattern extraction and LLM enrichment modules - src/agentready/services/llm_cache.py - LLM response caching - src/agentready/models/discovered_skill.py - Skill data model - tests/unit/learners/ - Unit tests for learning modules - .github/workflows/continuous-learning.yml - CI workflow - .github/CLAUDE_INTEGRATION.md - Integration documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

# [1.2.0](v1.1.2...v1.2.0) (2025-11-21) ### Features * Add automated demo command for AgentReady ([#24](#24)) ([f4e89d9](f4e89d9)), closes [#1](#1) [#25](#25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring)

github-actions · 2025-11-21T19:21:24Z

🎉 This PR is included in version 1.2.0 🎉

The release is available on GitHub release

Your semantic-release bot 📦🚀

* feat: Add automated demo command for AgentReady Implements P0 feature to showcase AgentReady capabilities with a single command. Features: - agentready demo command creates sample repository and runs assessment - Supports Python (default) and JavaScript demo repositories - Real-time progress indicators with color-coded output (✓/✗/⊘) - Auto-opens HTML report in browser (optional --no-browser flag) - Generates HTML, Markdown, and JSON reports in .agentready-demo/ - Options: --language, --no-browser, --keep-repo Implementation: - Created src/agentready/cli/demo.py with demo command logic - Registered demo command in CLI main.py - Added comprehensive unit tests in tests/unit/test_demo.py - Demo completes in ~3-5 seconds with 25 attribute assessments Perfect for presentations, stakeholder demos, and user onboarding. Closes #1 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com> * fix: install AgentReady from source in CI workflow The workflow was attempting to install agentready from PyPI, but the package has never been published. Changed to install from checked-out repository source using 'pip install -e .' instead. This ensures CI tests the actual code in the PR rather than a potentially stale PyPI version. Fixes GitHub Actions workflow failure on PR #24. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add manual PyPI publishing workflow Added workflow_dispatch workflow for manual PyPI publishing with: - Dry run mode (publishes to TestPyPI for testing) - Production mode (publishes to PyPI) - Optional version override - Automatic GitHub release creation - Built-in validation with twine check - Secure: Uses environment variables to prevent command injection Usage: 1. Configure TEST_PYPI_TOKEN and PYPI_TOKEN secrets 2. Go to Actions → "Publish to PyPI" → Run workflow 3. Choose dry run (TestPyPI) or production (PyPI) Future enhancement tracked in issue #25 for automated integration with semantic-release workflow. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: implement continuous learning loop with LLM-powered skill extraction Add comprehensive learning system that extracts high-quality skills from assessments: - New 'learn' CLI command with heuristic and LLM enrichment modes - Claude API integration for detailed skill analysis and instruction generation - LLM response caching system (7-day TTL) to reduce API costs - Code sampling for real repository examples - Pattern extraction from high-scoring assessment attributes - Support for multiple output formats (JSON, SKILL.md, GitHub issues) Technical additions: - src/agentready/cli/learn.py - Main learning command implementation - src/agentready/learners/ - Pattern extraction and LLM enrichment modules - src/agentready/services/llm_cache.py - LLM response caching - src/agentready/models/discovered_skill.py - Skill data model - tests/unit/learners/ - Unit tests for learning modules - .github/workflows/continuous-learning.yml - CI workflow - .github/CLAUDE_INTEGRATION.md - Integration documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com>

# [1.2.0](v1.1.2...v1.2.0) (2025-11-21) ### Features * Add automated demo command for AgentReady ([#24](#24)) ([f4e89d9](f4e89d9)), closes [#1](#1) [#25](#25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring)

…ration (#29) * feat: Add Repomix integration for AI-friendly repository context generation Implements comprehensive Repomix integration as specified in coldstart prompt 008-repomix-integration.md. This feature enables automated repository context generation for AI consumption and improves AI-assisted development workflows. ## Features Added ### Core Components - **RepomixService** (`src/agentready/services/repomix.py`) - Configuration generation (repomix.config.json) - Ignore file generation (.repomixignore) - Repomix execution wrapper with error handling - Freshness checking (7-day default staleness threshold) - Output file management and discovery - **CLI Command** (`src/agentready/cli/repomix.py`) - `agentready repomix-generate` - Main command - `--init` - Initialize Repomix configuration - `--format` - Output format selection (markdown/xml/json/plain) - `--check` - Verify output freshness without regeneration - `--max-age` - Configurable staleness threshold - **Bootstrap Integration** (`src/agentready/cli/bootstrap.py`) - Added `--repomix` flag to bootstrap command - Auto-generates repomix.config.json and .repomixignore - Creates GitHub Actions workflow for automation - **GitHub Actions Workflow** (`src/agentready/templates/bootstrap/workflows/repomix-update.yml.j2`) - Auto-regenerates on push to main and PRs - Weekly scheduled runs (Mondays 9 AM UTC) - Manual trigger support via workflow_dispatch - PR comments when Repomix output changes - **Repomix Assessor** (`src/agentready/assessors/repomix.py`) - Tier 3 attribute (weight: 0.02) - Checks for configuration file existence - Validates output freshness (< 7 days) - Provides detailed remediation guidance ### Testing - Comprehensive unit tests (21 test cases) - RepomixService tests with mocking - Installation detection - Config/ignore generation - Freshness checks - Command execution - RepomixConfigAssessor tests - Multiple assessment scenarios - Pass/fail/partial compliance ### Documentation - Updated repomix-output.md (1.8M, 420k tokens, 156 files) - AgentReady self-assessment: **80.0/100 (Gold)** 🥇 ## Technical Details ### Architecture - Follows existing AgentReady patterns - Strategy pattern for assessor - Service layer for business logic - Template-based workflow generation ### Integration Points - Registered in main CLI (`src/agentready/cli/main.py`) - Added to bootstrap generator (`src/agentready/services/bootstrap.py`) - Included in assessor list (Tier 3 Important) ### Configuration Management - Smart defaults for Python projects - Customizable ignore patterns - Aligned with existing .gitignore patterns - Security scanning enabled by default ## Use Cases ```bash # Initialize Repomix for repository agentready repomix-generate --init # Generate AI-friendly context agentready repomix-generate # Bootstrap new repo with Repomix agentready bootstrap --repomix # Check if output is fresh agentready repomix-generate --check ``` ## Related - Coldstart Prompt: `coldstart-prompts/08-repomix-integration.md` - Priority: P4 (Enhancement) - Category: AI-Assisted Development Tools 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * chore(release): 1.1.2 [skip ci] ## [1.1.2](v1.1.1...v1.1.2) (2025-11-21) ### Bug Fixes * correct GitHub repository link in site navigation ([5492278](5492278)) * feat: Add automated demo command for AgentReady (#24) * feat: Add automated demo command for AgentReady Implements P0 feature to showcase AgentReady capabilities with a single command. Features: - agentready demo command creates sample repository and runs assessment - Supports Python (default) and JavaScript demo repositories - Real-time progress indicators with color-coded output (✓/✗/⊘) - Auto-opens HTML report in browser (optional --no-browser flag) - Generates HTML, Markdown, and JSON reports in .agentready-demo/ - Options: --language, --no-browser, --keep-repo Implementation: - Created src/agentready/cli/demo.py with demo command logic - Registered demo command in CLI main.py - Added comprehensive unit tests in tests/unit/test_demo.py - Demo completes in ~3-5 seconds with 25 attribute assessments Perfect for presentations, stakeholder demos, and user onboarding. Closes #1 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com> * fix: install AgentReady from source in CI workflow The workflow was attempting to install agentready from PyPI, but the package has never been published. Changed to install from checked-out repository source using 'pip install -e .' instead. This ensures CI tests the actual code in the PR rather than a potentially stale PyPI version. Fixes GitHub Actions workflow failure on PR #24. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add manual PyPI publishing workflow Added workflow_dispatch workflow for manual PyPI publishing with: - Dry run mode (publishes to TestPyPI for testing) - Production mode (publishes to PyPI) - Optional version override - Automatic GitHub release creation - Built-in validation with twine check - Secure: Uses environment variables to prevent command injection Usage: 1. Configure TEST_PYPI_TOKEN and PYPI_TOKEN secrets 2. Go to Actions → "Publish to PyPI" → Run workflow 3. Choose dry run (TestPyPI) or production (PyPI) Future enhancement tracked in issue #25 for automated integration with semantic-release workflow. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: implement continuous learning loop with LLM-powered skill extraction Add comprehensive learning system that extracts high-quality skills from assessments: - New 'learn' CLI command with heuristic and LLM enrichment modes - Claude API integration for detailed skill analysis and instruction generation - LLM response caching system (7-day TTL) to reduce API costs - Code sampling for real repository examples - Pattern extraction from high-scoring assessment attributes - Support for multiple output formats (JSON, SKILL.md, GitHub issues) Technical additions: - src/agentready/cli/learn.py - Main learning command implementation - src/agentready/learners/ - Pattern extraction and LLM enrichment modules - src/agentready/services/llm_cache.py - LLM response caching - src/agentready/models/discovered_skill.py - Skill data model - tests/unit/learners/ - Unit tests for learning modules - .github/workflows/continuous-learning.yml - CI workflow - .github/CLAUDE_INTEGRATION.md - Integration documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com> * chore(release): 1.2.0 [skip ci] # [1.2.0](v1.1.2...v1.2.0) (2025-11-21) ### Features * Add automated demo command for AgentReady ([#24](#24)) ([f4e89d9](f4e89d9)), closes [#1](#1) [#25](#25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring) * test(bootstrap): add comprehensive test coverage for bootstrap feature (#26) Add 32 tests (19 unit, 13 integration) for bootstrap functionality: - BootstrapGenerator service: 100% coverage (101/101 lines) - Bootstrap CLI: 92% coverage (37/40 lines) - Tests cover dry-run mode, file generation, language detection, template rendering, error handling, and edge cases Also fix ambiguous variable name linting error in code_quality.py (line 300: l → line for better readability) All tests passing, all linters clean. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude <noreply@anthropic.com> * feat: add report header with repository metadata (#28) Add prominent report header showing repository context and assessment metadata to all report formats (HTML, Markdown, JSON). Changes: - Create AssessmentMetadata model to capture execution context - AgentReady version from package metadata - Assessment timestamp (ISO 8601 and human-readable) - Executed by (username@hostname) - CLI command used - Working directory - Update Assessment model with optional metadata field - Implement metadata collection in Scanner service - Get version from importlib.metadata - Reconstruct command from sys.argv - Capture user and hostname from environment - Update all reporters to display metadata - HTML: Two-column header (repo info + meta info) - Markdown: Prominent header with all metadata fields - JSON: Metadata object at top level - Add comprehensive unit tests (4 new tests, all passing) - All 37 tests passing (34 unit + 3 integration) Acceptance criteria met: ✅ User can identify repository assessed (name, path, branch, commit) ✅ Timestamp shows when assessment was run ✅ Git context visible in all reports ✅ AgentReady version tracked for reproducibility ✅ Execution context captured (user@host, command, cwd) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude <noreply@anthropic.com> * chore(release): 1.3.0 [skip ci] # [1.3.0](v1.2.0...v1.3.0) (2025-11-21) ### Features * add report header with repository metadata ([#28](#28)) ([7a8b34a](7a8b34a)) --------- Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: semantic-release-bot <semantic-release-bot@martynus.net> Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com>

# [1.4.0](v1.3.0...v1.4.0) (2025-11-21) ### Features * Add Repomix integration for AI-friendly repository context generation ([#29](#29)) ([92bdde1](92bdde1)), closes [#24](#24) [#1](#1) [#25](#25) [hi#quality](https://github.com/hi/issues/quality) [hi#scoring](https://github.com/hi/issues/scoring)

chatgpt-codex-connector bot reviewed Nov 21, 2025

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jeremyeder mentioned this pull request Nov 21, 2025

Publish AgentReady to PyPI with automated release workflow #25

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jeremyeder and others added 2 commits November 21, 2025 07:48

jeremyeder merged commit f4e89d9 into main Nov 21, 2025
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github-actions bot added the released label Nov 21, 2025

jeremyeder mentioned this pull request Nov 22, 2025

feat: Implement CICDPipelineVisibilityAssessor #85

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jeremyeder commented Nov 21, 2025

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