datapowers 📊

Data Mining Superpowers for AI Agents.

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datapowers is a professional data mining and machine learning workflow protocol for Claude Code, Gemini CLI, and autonomous agents. It bridges software engineering rigor (TDD) with data science's statistical sovereignty — enforcing leakage prevention, three-layer validation, and evidence-based delivery as non-negotiable pipeline gates.

🔍 Why datapowers?

Statistical Integrity > Model Metrics. datapowers is not a prompt collection — it is an Audit Protocol.

• 🛡️ Leakage Defense — Systematic temporal and preprocessing leakage audits at the source, before any evaluation.
• ⚖️ Three-Layer Validation — Physical (Schema), Logical (Business Rules), and Statistical (Distribution Drift) gates block training on bad data.
• 🚫 Point Estimate Illusions — Mandatory confidence intervals and significance tests on all reported results.
• 🧠 Hypothesis-Driven — Forces falsifiable goal-setting and a baseline expectation before touching raw data.

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⚙️ How It Works: Skill Loading Flow

datapowers uses a hook-based on-demand loading architecture inspired by superpowers.

Session Start
     │
     ▼
hooks/session-start fires
     │
     ├── Reads skills/using-datapowers/SKILL.md
     │
     └── Injects it as session context (EXTREMELY_IMPORTANT)
               │
               ▼
         Agent now knows:
         • All 20 available skills
         • Trigger keyword for each skill
         • When to invoke each skill
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         Analyst asks a question
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         Agent matches trigger → invokes Skill tool
               │
               ├── Loads skills/<name>/SKILL.md (Iron Laws, Hard Gates, Procedure)
               │
               └── Loads supplementary files as needed:
                   • reference docs (e.g., assertion-anti-patterns.md)
                   • pressure tests (test-pressure-1.md, ...)
                   • agent prompt templates (analyst-prompt.md, ...)
                   • utility scripts (pipeline-pollution-detection.sh)

Key design principles:

• using-datapowers is always loaded — it is the routing table for all other skills.
• All other skills are loaded on demand — no unnecessary context inflation.
• Supplementary files are scoped — each skill only loads what it needs.
• Session state persists in artifacts/analysis_manifest.json — the agent can resume any session without losing context.

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📋 Skills Reference

All 20 skills, organized by phase.

Phase 0 — Entry & State

Skill	When to Use	Key Output
using-datapowers	Always loaded at session start	Routing table: all skills + trigger keywords
analysis-manifest	Session start; "Where are we?"; after brainstorming	artifacts/analysis_manifest.json — single source of truth for session state

Phase 1 — Design

Skill	When to Use	Key Output
brainstorming	"Analyze X", "Design spec", "Hypothesis"	docs/datapowers/specs/ design doc with ≥3 falsifiable hypotheses
writing-analysis-plans	After brainstorming approval	docs/datapowers/plans/ plan with 15–30 min tasks, no placeholders

Phase 2 — Data Understanding

Skill	When to Use	Key Output
data-profiling	New dataset; before any subagent dispatch	artifacts/data_profile.md — high-density PII-free profile
data-exploration	First-time dataset exploration; EDA requested	docs/datapowers/eda/ report; leakage candidate flags
data-validation	Before any model training or feature engineering	Pandera validation report; BLOCK / PROCEED verdict

Phase 3 — Feature Engineering & Modeling

Skill	When to Use	Key Output
leakage-guard	Temporal dataset; before feature engineering review	BLOCKED / NEEDS_HUMAN_REVIEW / APPROVED verdict
feature-engineering	Building or transforming features	Fitted transformers saved to artifacts/; Feature Registry updated
test-driven-data-science	Before any model.fit() call	Three-layer assertion results; training blocked on CRITICAL failures
model-selection	Choosing between candidate models	Baseline comparison table; Optuna HPO results (≥50 trials)
model-evaluation	Final test set evaluation	Bootstrap CIs; SHAP summary; one-time test set gate

Phase 4 — Execution & Review

Skill	When to Use	Key Output
executing-plans	"Start tasks", "Follow plan"	Two-stage review per task: Statistical → Code Quality
subagent-driven-analysis	Multi-task parallel analysis	Parallel analyst subagents with isolated context; review gating
requesting-statistical-review	"Audit results", "Significance", after task completion	Statistical review verdict: APPROVED / ISSUES FOUND / BLOCKED
debugging-pipelines	Pipeline error; unexpected model behavior; performance drop	Root cause investigation log; PSI drift report

Phase 5 — Delivery

Skill	When to Use	Key Output
verification-before-delivery	"Done", "Complete", before any delivery	Artifact integrity checklist; reproducibility confirmation
report-writing	Final stakeholder report	Reproducibility-header report with CIs and significance tests
finishing-an-analysis-branch	Analysis complete; ready to deliver	Commit / PR / archive options; manifest-gated delivery

Meta

Skill	When to Use	Key Output
writing-data-skills	"New skill", "Add skill", "Contribute skill"	New SKILL.md passing Statistical Pressure Test (3 scenarios)

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🚀 The Core Workflow

brainstorming → writing-analysis-plans
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data-profiling → data-exploration → data-validation
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leakage-guard → feature-engineering → test-driven-data-science
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model-selection → model-evaluation
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executing-plans (with requesting-statistical-review per task)
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      ▼
verification-before-delivery → report-writing → finishing-an-analysis-branch

At any step: analysis-manifest tracks completed stages. debugging-pipelines handles any unexpected failures. subagent-driven-analysis can parallelize independent tasks.

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🛠️ Installation

Claude Code

# Install the plugin
/plugin install https://github.com/zpower426/datapowers

# Or clone and install locally
git clone https://github.com/zpower426/datapowers
cd datapowers
/plugin install .

How it works: .claude-plugin/plugin.json registers the hooks directory. On every session start, hooks/session-start fires and injects using-datapowers skill content into the session context automatically.

Gemini CLI

gemini extensions install https://github.com/zpower426/datapowers

The GEMINI.md file and gemini-extension.json manifest handle skill path registration.

OpenCode

git clone https://github.com/zpower426/datapowers ~/.opencode/plugins/datapowers

The .opencode/plugins/datapowers.js plugin injects bootstrap context via system prompt transform and auto-registers the skills directory.

Cursor / Codex

See .cursor-plugin/plugin.json and .codex/INSTALL.md for setup instructions.

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⚖️ Iron Laws

Domain	Iron Law
EDA	NO MODELING WITHOUT EXPLORATORY DATA ANALYSIS
Validation	NO TRAINING WITHOUT DATA QUALITY VALIDATION
Leakage	NO TRANSFORMERS FIT ON FULL DATASET BEFORE TRAIN/TEST SPLIT
Evaluation	TEST SET EVALUATED EXACTLY ONCE — AT THE END
Delivery	NO DELIVERY WITHOUT CONFIDENCE INTERVALS AND SIGNIFICANCE TESTS
Review	STATISTICAL AUDIT ALWAYS PRECEDES CODE QUALITY REVIEW

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📈 Star History

🤝 Contributing

Contributions welcome. Read the writing-data-skills skill first — every new skill must pass the Statistical Pressure Test (balanced data, 1:100 imbalance, n < 200 small sample) before merge.

❤️ Acknowledgments

datapowers is inspired by and built on the shoulders of superpowers by Jesse Vincent (@obra).

The hook-based on-demand skill loading architecture, Iron Laws pattern, and Hard Gates design are all adapted from the superpowers family — extended here for the specific demands of statistical rigor in data science.

📜 License

MIT License