# PMAT

<div align="center">
  <img src="docs/images/pmat-logo.svg" alt="PMAT" width="500">

  <p><strong>Zero-configuration AI context generation for any codebase</strong></p>

[![Crates.io](https://img.shields.io/crates/v/pmat.svg)](https://crates.io/crates/pmat)
[![Documentation](https://docs.rs/pmat/badge.svg)](https://docs.rs/pmat)
[![Tests](https://img.shields.io/badge/tests-4600%2B%20passing-brightgreen)](https://github.com/paiml/paiml-mcp-agent-toolkit)
[![Coverage](https://img.shields.io/badge/coverage-95%25%2B-brightgreen)](https://github.com/paiml/paiml-mcp-agent-toolkit)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Rust](https://img.shields.io/badge/rust-1.83+-orange.svg)](https://www.rust-lang.org)
[![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.PMAT-blue)](https://zenodo.org/records/pmat)

[Getting Started](#getting-started) | [Features](#features) | [Examples](#examples) | [Documentation](https://paiml.github.io/pmat-book/)

</div>

---

## What is PMAT?

**PMAT** (Pragmatic Multi-language Agent Toolkit) provides everything needed to analyze code quality and generate AI-ready context:

- **Context Generation** - Deep analysis for Claude, GPT, and other LLMs
- **Technical Debt Grading** - A+ through F scoring with 6 orthogonal metrics
- **Mutation Testing** - Test suite quality validation (85%+ kill rate)
- **Repository Scoring** - Quantitative health assessment (0-211 scale)
- **Git History RAG** - Semantic search across commit history with RRF fusion
- **Semantic Search** - Natural language code discovery
- **MCP Integration** - 19 tools for Claude Code, Cline, and AI agents
- **Quality Gates** - Pre-commit hooks, CI/CD integration
- **17+ Languages** - Rust, TypeScript, Python, Go, Java, C/C++, and more

Part of the [PAIML Stack](https://github.com/paiml), following Toyota Way quality principles (Jidoka, Genchi Genbutsu, Kaizen).

### Annotated Code Search

<div align="center">
  <img src="docs/images/pmat-query-screenshot.png" alt="pmat query annotated output" width="800">
  <p><em><code>pmat query "cache invalidation" --churn --duplicates --entropy --faults</code></em></p>
</div>

Every result includes TDG grade, Big-O complexity, git churn, code clones, pattern diversity, fault annotations, call graph, and syntax-highlighted source.

## Getting Started

Add to your system:

```bash
# Install from crates.io
cargo install pmat

# Or from source (latest)
git clone https://github.com/paiml/paiml-mcp-agent-toolkit
cd paiml-mcp-agent-toolkit && cargo install --path server
```

### Basic Usage

```bash
# Generate AI-ready context
pmat context --output context.md --format llm-optimized

# Analyze code complexity
pmat analyze complexity

# Grade technical debt (A+ through F)
pmat analyze tdg

# Score repository health
pmat repo-score .

# Run mutation testing
pmat mutate --target src/
```

### MCP Server Mode

```bash
# Start MCP server for Claude Code, Cline, etc.
pmat mcp
```

## Features

### Context Generation

Generate comprehensive context for AI assistants:

```bash
pmat context                           # Basic analysis
pmat context --format llm-optimized    # AI-optimized output
pmat context --include-tests           # Include test files
```

### Technical Debt Grading (TDG)

Six orthogonal metrics for accurate quality assessment:

```bash
pmat analyze tdg                       # Project-wide grade
pmat analyze tdg --include-components  # Per-component breakdown
pmat tdg baseline create               # Create quality baseline
pmat tdg check-regression              # Detect quality degradation
```

**Grading Scale:**
- **A+/A**: Excellent quality, minimal debt
- **B+/B**: Good quality, manageable debt
- **C+/C**: Needs improvement
- **D/F**: Significant technical debt

### Mutation Testing

Validate test suite effectiveness:

```bash
pmat mutate --target src/lib.rs        # Single file
pmat mutate --target src/ --threshold 85  # Quality gate
pmat mutate --failures-only            # CI optimization
```

**Supported Languages:** Rust, Python, TypeScript, JavaScript, Go, C++

### Repository Health Scoring

Evidence-based quality metrics (0-211 scale):

```bash
pmat rust-project-score                # Fast mode (~3 min)
pmat rust-project-score --full         # Comprehensive (~10-15 min)
pmat repo-score . --deep               # Full git history
```

### Workflow Prompts

Pre-configured AI prompts enforcing EXTREME TDD:

```bash
pmat prompt --list                     # Available prompts
pmat prompt code-coverage              # 85%+ coverage enforcement
pmat prompt debug                      # Five Whys analysis
pmat prompt quality-enforcement        # All quality gates
```

### Git History RAG

Search git history by intent using TF-IDF semantic embeddings:

```bash
# Fuse git history into code search
pmat query "fix memory leak" -G

# Search with churn, clones, entropy, faults
pmat query "error handling" --churn --duplicates --entropy --faults
```

```bash
# Run the example
cargo run --example git_history_demo
```

### Git Hooks

Automatic quality enforcement:

```bash
pmat hooks install                     # Install pre-commit hooks
pmat hooks install --tdg-enforcement   # With TDG quality gates
pmat hooks status                      # Check hook status
```

## Examples

### Generate Context for AI

```bash
# For Claude Code
pmat context --output context.md --format llm-optimized

# With semantic search
pmat embed sync ./src
pmat semantic search "error handling patterns"
```

### CI/CD Integration

```yaml
# .github/workflows/quality.yml
name: Quality Gates
on: [push, pull_request]

jobs:
  quality:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: cargo install pmat
      - run: pmat analyze tdg --fail-on-violation --min-grade B
      - run: pmat mutate --target src/ --threshold 80
```

### Quality Baseline Workflow

```bash
# 1. Create baseline
pmat tdg baseline create --output .pmat/baseline.json

# 2. Check for regressions
pmat tdg check-regression \
  --baseline .pmat/baseline.json \
  --max-score-drop 5.0 \
  --fail-on-regression
```

## Architecture

```
pmat/
├── server/           CLI and MCP server
│   ├── src/
│   │   ├── cli/      Command handlers
│   │   ├── services/ Analysis engines
│   │   ├── mcp/      MCP protocol
│   │   └── tdg/      Technical Debt Grading
├── crates/
│   └── pmat-dashboard/  Pure WASM dashboard
└── docs/
    └── specifications/  Technical specs
```

## Quality

| Metric | Value |
|--------|-------|
| Tests | 4600+ passing |
| Coverage | 95%+ |
| Mutation Score | >80% |
| Languages | 17+ supported |
| MCP Tools | 19 available |

### Falsifiable Quality Commitments

Per [Popper's demarcation criterion](https://en.wikipedia.org/wiki/Demarcation_problem), all claims are measurable and testable:

| Commitment | Threshold | Verification Method |
|------------|-----------|---------------------|
| **Context Generation** | < 5 seconds for 10K LOC project | `time pmat context` on test corpus |
| **Memory Usage** | < 500 MB for 100K LOC analysis | Measured via `heaptrack` in CI |
| **Test Coverage** | ≥ 85% line coverage | `cargo llvm-cov` (CI enforced) |
| **Mutation Score** | ≥ 80% killed mutants | `pmat mutate --threshold 80` |
| **Build Time** | < 3 minutes incremental | `cargo build --timings` |
| **CI Pipeline** | < 15 minutes total | GitHub Actions workflow timing |
| **Binary Size** | < 50 MB release binary | `ls -lh target/release/pmat` |
| **Language Parsers** | All 17 languages parse without panic | Fuzz testing in CI |

**How to Verify:**

```bash
# Run self-assessment with Popper Falsifiability Score
pmat popper-score --verbose

# Individual commitment verification
cargo llvm-cov --html        # Coverage ≥85%
pmat mutate --threshold 80   # Mutation ≥80%
cargo build --timings        # Build time <3min
```

**Failure = Regression:** Any commitment violation blocks CI merge.

### Benchmark Results (Statistical Rigor)

All benchmarks use Criterion.rs with proper statistical methodology:

| Operation | Mean | 95% CI | Std Dev | Sample Size |
|-----------|------|--------|---------|-------------|
| Context (1K LOC) | 127ms | [124, 130] | ±12.3ms | n=1000 runs |
| Context (10K LOC) | 1.84s | [1.79, 1.90] | ±156ms | n=500 runs |
| TDG Scoring | 156ms | [148, 164] | ±18.2ms | n=500 runs |
| Complexity Analysis | 23ms | [22, 24] | ±3.1ms | n=1000 runs |

**Comparison Baselines (vs. Alternatives):**

| Metric | PMAT | ctags | tree-sitter | Effect Size |
|--------|------|-------|-------------|-------------|
| 10K LOC parsing | 1.84s | 0.3s | 0.8s | d=0.72 (medium) |
| Memory (10K LOC) | 287MB | 45MB | 120MB | - |
| Semantic depth | Full | Syntax only | AST only | - |

See [docs/BENCHMARKS.md](docs/BENCHMARKS.md) for complete statistical analysis.

### ML/AI Reproducibility

PMAT uses ML for semantic search and embeddings. All ML operations are reproducible:

**Random Seed Management:**
- Embedding generation uses fixed seed (SEED=42) for deterministic outputs
- Clustering operations use fixed seed (SEED=12345)
- Seeds documented in [docs/ml/REPRODUCIBILITY.md](docs/ml/REPRODUCIBILITY.md)

**Model Artifacts:**
- Pre-trained models from HuggingFace (all-MiniLM-L6-v2)
- Model versions pinned in Cargo.toml
- Hash verification on download

## Dataset Sources

PMAT does not train models but uses these data sources for evaluation:

| Dataset | Source | Purpose | Size |
|---------|--------|---------|------|
| CodeSearchNet | GitHub/Microsoft | Semantic search benchmarks | 2M functions |
| PMAT-bench | Internal | Regression testing | 500 queries |

Data provenance and licensing documented in [docs/ml/REPRODUCIBILITY.md](docs/ml/REPRODUCIBILITY.md).

## Sovereign Stack

PMAT is built on the PAIML Sovereign Stack - pure-Rust, SIMD-accelerated libraries:

| Library | Purpose | Version |
|---------|---------|---------|
| [aprender](https://crates.io/crates/aprender) | ML library (text similarity, clustering, topic modeling) | 0.24.0 |
| [trueno](https://crates.io/crates/trueno) | SIMD compute library for matrix operations | 0.11.0 |
| [trueno-graph](https://crates.io/crates/trueno-graph) | GPU-first graph database (PageRank, Louvain, CSR) | 0.1.7 |
| [trueno-rag](https://crates.io/crates/trueno-rag) | RAG pipeline with VectorStore | 0.1.8 |
| [trueno-db](https://crates.io/crates/trueno-db) | Embedded analytics database | 0.3.10 |
| [trueno-viz](https://crates.io/crates/trueno-viz) | Terminal graph visualization | 0.1.17 |
| [trueno-zram-core](https://crates.io/crates/trueno-zram-core) | SIMD LZ4/ZSTD compression (optional) | 0.3.0 |
| **pmat** | Code analysis toolkit | 2.213.4 |

**Key Benefits:**
- Pure Rust (no C dependencies, no FFI)
- SIMD-first (AVX2, AVX-512, NEON auto-detection)
- 2-4x speedup on graph algorithms via aprender adapter

## Documentation

- [PMAT Book](https://paiml.github.io/pmat-book/) - Complete guide
- [API Reference](https://docs.rs/pmat) - Rust API docs
- [MCP Tools](docs/mcp/TOOLS.md) - MCP integration guide
- [Specifications](docs/specifications/) - Technical specs

## License

MIT License - see [LICENSE](LICENSE) for details.

---

<div align="center">
  <sub>Built with Extreme TDD | Part of <a href="https://github.com/paiml">PAIML</a></sub>
</div>
