[](https://github.com/paiml/organizational-intelligence-plugin/actions/workflows/ci.yml) [](https://crates.io/crates/organizational-intelligence-plugin) [](https://docs.rs/organizational-intelligence-plugin)

Organizational Intelligence Plugin (OIP)

A plugin for pmat that analyzes GitHub organizations to detect defect patterns, measure code quality, and generate actionable intelligence for software development teams.

Table of Contents

• Features
• Installation
• Usage
• Contributing

---

Overview

Organizational Intelligence Plugin (OIP) mines Git history and integrates with pmat's Technical Debt Gradient (TDG) analysis to:

• Detect defect patterns across 10 categories (Configuration Errors, Security Vulnerabilities, Type Errors, etc.)
• Measure code quality via pmat TDG integration (0-100 score, higher is better)
• Generate privacy-safe summaries with automated PII stripping
• Provide fast PR reviews using stateful baselines (<30s vs 10+ minutes)
• Enable data-driven decisions for technical debt prioritization

Features

✅ Phase 1 - Core Analysis (oip analyze)

• Analyze GitHub organizations for defect patterns
• Integrate pmat TDG quality scores
• Generate comprehensive YAML reports

✅ Phase 2 - Summarization (oip summarize)

• Automated PII stripping (commit hashes, author emails)
• Frequency filtering and top-N category selection
• Privacy-safe summaries ready for AI consumption

✅ Phase 3 - PR Review (oip review-pr)

• Fast PR reviews using stateful baselines (<30s)
• Context-aware warnings based on organizational history
• Multiple output formats (Markdown, JSON)

🚀 Phase 1 GPU Extension (oip-gpu) - NEW!

• GPU-accelerated correlation analysis for defect patterns
• SIMD-optimized feature extraction (trueno backend)
• Benchmark suite with criterion (10-50× speedup targets)
• Complete GitHub → Features → Storage pipeline
• See: GPU Quick Start | Full Spec

🔌 First-Class pmat Plugin - NEW!

• Enabled by default in pmat demo-score --with-oip
• Tarantula SBFL fault localization for test failures
• SZZ bug-introducing commit analysis
• Historical quality trend tracking
• See: pmat Demo/Book Scoring Spec

Toyota Way Principles

This tool is built following Toyota Production System principles:

• Genchi Genbutsu (Go and See): Analyzes actual commit history, not surveys
• Kaizen (Continuous Improvement): Weekly reports track improvement over time
• Jidoka (Build Quality In): Identifies defect patterns to fix root causes
• Muda/Muri/Mura Elimination: Automates manual work, prevents overburden, smooths workflow

Installation

Prerequisites

• Rust 1.70 or higher
• Git
• pmat (optional, for TDG integration)
• GitHub Personal Access Token (for higher rate limits)

From Source

git clone https://github.com/paiml/organizational-intelligence-plugin
cd organizational-intelligence-plugin
cargo build --release

# Binary available at target/release/oip
export PATH=$PATH:$(pwd)/target/release

Via Cargo (once published)

cargo install organizational-intelligence-plugin

Setting Up GitHub Token

# Create a GitHub Personal Access Token at:
# https://github.com/settings/tokens
# Required scopes: repo (for private repos) or public_repo (for public only)

export GITHUB_TOKEN=ghp_your_token_here

# Add to ~/.bashrc or ~/.zshrc for persistence
echo 'export GITHUB_TOKEN=ghp_your_token_here' >> ~/.bashrc

Usage

Quick Start

# 1. Analyze your organization
oip analyze --org YOUR_ORG --output analysis.yaml

# 2. Generate privacy-safe summary
oip summarize --input analysis.yaml --output summary.yaml --strip-pii

# 3. Review a PR (requires baseline)
oip review-pr --baseline summary.yaml --files src/config.rs,src/auth.rs

Phase 1: Analyze Organization

# Analyze all repositories in an organization
oip analyze --org paiml --output paiml-analysis.yaml

# With verbose logging
oip analyze --org paiml --output paiml-analysis.yaml --verbose

# Limit concurrent analysis (default: 10)
oip analyze --org paiml --output paiml-analysis.yaml --max-concurrent 5

Output: YAML report with:

• Defect patterns by category (frequency, confidence)
• TDG quality scores (requires pmat)
• Code churn metrics (lines changed, files per commit)
• Example commits (⚠️ contains PII - use Phase 2 to strip)

Phase 2: Summarize for Sharing

# Generate privacy-safe summary
oip summarize \
  --input analysis.yaml \
  --output summary.yaml \
  --strip-pii \
  --top-n 10 \
  --min-frequency 5

# Include anonymized examples
oip summarize \
  --input analysis.yaml \
  --output summary.yaml \
  --strip-pii \
  --include-examples

Output: Clean YAML with:

• Top N defect categories by frequency
• PII redacted (commit_hash: REDACTED, author: REDACTED)
• Quality thresholds (TDG 85+, coverage 85%+)
• Safe for sharing with AI tools

Phase 3: PR Review

# One-time: Create baseline (run weekly)
oip analyze --org myorg --output baseline.yaml
oip summarize --input baseline.yaml --output baseline-summary.yaml

# On every PR: Fast review (<30s)
oip review-pr \
  --baseline baseline-summary.yaml \
  --files src/config.rs,src/auth.rs \
  --format markdown \
  --output pr-review.md

# Output to stdout for CI integration
oip review-pr \
  --baseline baseline-summary.yaml \
  --files $(git diff --name-only HEAD~1) \
  --format json

Phase 1 GPU: Accelerated Analysis

# Analyze repository with GPU-accelerated features
oip-gpu analyze --repo rust-lang/rust --output features.db

# Run performance benchmarks
oip-gpu benchmark --suite all

# Force SIMD backend (CPU)
oip-gpu analyze --repo owner/repo --backend simd --output out.db

Output: Feature vectors ready for GPU correlation analysis

See GPU Quick Start for detailed examples.

Output: Context-aware warnings based on organizational defect patterns

Examples

Sprint Planning

# Generate current analysis
oip analyze --org myorg --output sprint-data.yaml

# Identify high-priority technical debt
# (High frequency + Low TDG score = urgent refactoring needed)
cat sprint-data.yaml | grep -A10 "frequency: 2[0-9]"

Weekly Baseline Updates

#!/bin/bash
# weekly-baseline.sh - Run via cron every Monday

export GITHUB_TOKEN=your_token
ORG=myorg
DATE=$(date +%Y-%m-%d)

oip analyze --org $ORG --output "baselines/full-$DATE.yaml"
oip summarize \
  --input "baselines/full-$DATE.yaml" \
  --output "baselines/summary-$DATE.yaml" \
  --strip-pii

echo "✅ Baseline updated: baselines/summary-$DATE.yaml"

CI/CD Integration

# .github/workflows/pr-review.yml
name: Organizational Intelligence PR Review

on: [pull_request]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install OIP
        run: cargo install organizational-intelligence-plugin

      - name: Review PR
        run: |
          FILES=$(gh pr diff ${{ github.event.pull_request.number }} --name-only | tr '\n' ',')
          oip review-pr \
            --baseline .oip/baseline.yaml \
            --files "$FILES" \
            --format markdown
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

pmat Demo/Book Scoring Integration

OIP integrates as a first-class plugin with pmat's demo-score command for educational repositories:

# Score a demo/book repository with OIP analysis (default when OIP installed)
pmat demo-score --with-oip

# OIP provides additional insights:
# - Tarantula SBFL: Fault localization for failing example tests
# - SZZ Analysis: Trace bug-introducing commits in examples
# - Historical Trends: Quality score evolution over time
# - Defect Classification: Pattern detection in example code

# Disable OIP integration
pmat demo-score --no-oip

# OIP-specific analysis modes
pmat demo-score --oip-analysis fault-localization
pmat demo-score --oip-analysis defect-patterns
pmat demo-score --oip-analysis historical-trends

Configuration (~/.config/pmat/config.toml):

[oip]
enabled = true                    # Auto-enable when oip binary found in PATH
binary_path = "oip"              # Custom path to OIP binary
fault_localization = true         # Enable Tarantula SBFL
szz_analysis = true              # Enable bug origin tracing
trend_tracking = true            # Enable historical analysis

See the Demo/Book Scoring Specification for full details.

Configuration

Makefile Targets

make help             # Show all available targets
make lint             # Quick lint check
make test-fast        # Fast unit tests (<5s)
make test-all         # All tests including integration
make coverage         # Generate HTML coverage report
make build            # Build release binary

Quality Gates

All code must pass:

• ✅ make lint - No clippy warnings
• ✅ make test-fast - All unit tests pass
• ✅ make coverage - 85%+ line coverage (currently: 86.65% ✅)
• ✅ pmat TDG score 85+ (currently: 96.4/100 ✅)

Development

Project Structure

organizational-intelligence-plugin/
├── src/
│   ├── analyzer.rs       # Organization analysis orchestration
│   ├── classifier.rs     # Defect pattern classification (10 categories)
│   ├── cli.rs           # Command-line interface (clap)
│   ├── git.rs           # Git history mining
│   ├── github.rs        # GitHub API integration (octocrab)
│   ├── pmat.rs          # pmat TDG integration
│   ├── pr_reviewer.rs   # PR review with stateful baselines
│   ├── report.rs        # YAML report generation
│   ├── summarizer.rs    # PII stripping and summarization
│   └── main.rs          # Entry point
├── tests/
│   └── cli_tests.rs     # CLI integration tests
├── docs/
│   └── how-to-integrate-as-plugin-with-pmat-improve-prompts-spec.md
├── Makefile             # Development workflow automation
└── Cargo.toml           # Rust dependencies

Running Tests

# Fast unit tests (recommended for development)
make test-fast

# All tests including network integration tests
make test-all

# Coverage report with HTML output
make coverage
make coverage-open  # Opens in browser

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Follow EXTREME TDD: Write tests first (RED-GREEN-REFACTOR)
4. Ensure all quality gates pass: make lint && make test-fast
5. Commit using conventional commits (feat:, fix:, docs:, etc.)
6. Push to your fork and submit a Pull Request

Code Standards:

• Minimum 85% test coverage for new code
• All clippy warnings must be resolved
• Follow Rust API guidelines
• Document public APIs with examples

Roadmap

Completed (Phase 1-3)

• ✅ Phase 1: Core analysis with pmat TDG integration
• ✅ Phase 2: Automated summarization with PII stripping
• ✅ Phase 3: Fast PR reviews using stateful baselines
• ✅ Phase 1 GPU: GPU-accelerated correlation analysis
• ✅ First-Class pmat Plugin: Integrated with pmat demo-score

Proposed (Phase 4)

• ⚪ AI Prompt Integration: Generate context-aware prompts for AI tools
• ⚪ DefectAwarePromptGenerator for paiml-mcp-agent-toolkit
• ⚪ MCP (Model Context Protocol) integration
• ⚪ Automated code review comments on GitHub PRs

See docs/how-to-integrate-as-plugin-with-pmat-improve-prompts-spec.md for detailed design specifications.

Troubleshooting

GitHub Rate Limits

Problem: API rate limit exceeded for...

Solution: Set GITHUB_TOKEN environment variable

export GITHUB_TOKEN=ghp_your_token_here

pmat Not Found

Problem: pmat analyze tdg fails

Solution: Install pmat

cargo install pmat

Analysis Takes Too Long

Problem: Analyzing large organizations is slow

Solution: Reduce max-concurrent flag

oip analyze --org large-org --output report.yaml --max-concurrent 3

See Also

• Cookbook

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built following Toyota Production System principles
• Inspired by empirical software engineering research
• Integrates with pmat for quality analysis
• Uses octocrab for GitHub API
• Uses git2-rs for Git operations

Support

• 📖 Documentation: docs/
• 🐛 Issues: GitHub Issues

Citation

If you use this tool in your research, please cite:

@software{organizational_intelligence_plugin,
  title = {Organizational Intelligence Plugin},
  author = {paiml},
  year = {2025},
  url = {https://github.com/paiml/organizational-intelligence-plugin},
  note = {A plugin for pmat that analyzes GitHub organizations for defect patterns}
}

---

Status: Phase 1-3 Complete | Grade: TDG 96.4/100 (A+) | Coverage: 86.65% (422 tests) ✅

Built with ❤️ following the Toyota Way