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patchwork

Mine your codebase. Generate CONVENTIONS.md. Stop AI agents from making up your style.

PyPI License: MIT Python 3.9+ agent-skills MCP


Every team that uses AI coding assistants hits the same wall: Claude writes getUserById in a codebase that uses get_user_by_id. Cursor creates components/userCard.tsx in a project that uses user-card.tsx. The agent invented a response shape that doesn't match the rest of the API.

You write a CLAUDE.md manually. It goes stale in two weeks. You write it again.

patchwork automates this. It scans your actual source code using AST analysis and detects what your team really does — not what you think you do.


What it detects

Category

What's mined

Naming

Functions, classes, variables, constants, files — with confidence score and real examples

Imports

Absolute vs relative, path aliases (@/, src/), barrel files, destructuring style

Structure

Source root, test layout, feature vs layer organisation, monorepo detection

Error Handling

try/except vs Result types, logging framework, custom exception naming, propagation style

Testing

Framework, assertion style, mocking library, coverage tool, fixture patterns

API Patterns

Response shape, route param style, ORM, async pattern, GraphQL/gRPC presence

Git Workflow

Commit message style, branch naming, co-change file pairs

Tech Stack

Frameworks, package manager, linters, formatters, type checker, build tool, scripts


Related MCP server: sourcebook

Quick start

pip install patchwork-conventions
cd your-project
patchwork scan

That's it. You'll get a CONVENTIONS.md like this:

# CONVENTIONS.md
> Auto-generated by patchwork on 2026-06-25

## Tech Stack
**Language:** python
**Runtime:** Python >=3.11
**Package Manager:** uv
**Frameworks:** fastapi, sqlalchemy
**Linters:** ruff
**Formatters:** ruff, black

## Naming Conventions

### Python
- **Functions:** `snake_case` (97% consistent)
  - Examples: `get_user`, `parse_response`, `create_session`
- **Classes:** `PascalCase` (100% consistent)
  - Examples: `UserService`, `AuthHandler`, `DatabaseClient`
- **Constants:** `SCREAMING_SNAKE`
  - Examples: `MAX_RETRIES`, `API_BASE_URL`
- **Files:** `snake_case`
- **Private prefix:** `_`
- **Test functions:** prefix `test_`

## Project Structure
**Source root:** `src/`
**Organisation:** layer-based
**Tests:** separate (`tests/`)

**Key directories:**
  - `src/` — source root
  - `tests/` — test suite
  - `migrations/` — database migrations

## Error Handling

### Python
- **Pattern:** try/except
- **Propagation:** raise
- **Logging:** `structlog`
- **Custom exception naming:** Error suffix
  - `ValidationError`, `AuthError`, `NotFoundError`

## Testing Conventions

### Python
- **Framework:** pytest
- **Coverage:** 34 test files / 89 source files (38% ratio)
- **Assertions:** `assert(...)`
- **Coverage tool:** `pytest-cov`
- **Patterns:** fixtures, factories

## Git Conventions
- **Commit style:** conventional commits
- **Examples:** `feat(auth): add JWT refresh`, `fix(api): handle null user`
- **Branch naming:** feature/name + fix/name

Why not argus or sourcebook?

Feature

patchwork

argus

sourcebook

AST-based naming analysis

✅ tree-sitter

❌ filesystem only

❌ not done

Confidence scores

✅ per-category

Real examples from your code

Counter-examples (inconsistencies)

Error handling pattern mining

API response shape detection

Co-change file pairs

Convention checking (check cmd)

MCP server with 8 tools

✅ (4 tools)

Watch mode

✅ (sync)

Zero LLM required

✅ (layer A)

Open source / MIT

❌ BSL


Commands

# Generate CONVENTIONS.md
patchwork scan

# Generate for a specific path
patchwork scan /path/to/project

# Generate AGENTS.md
patchwork scan --agents-md

# Append to CLAUDE.md
patchwork scan --claude-md

# Output JSON (for programmatic use)
patchwork scan --json

# Print to stdout (don't write file)
patchwork scan --stdout

# Limit to specific languages
patchwork scan --lang python --lang typescript

# Re-scan and update, preserving manual edits
patchwork update

# Show what would change
patchwork diff

# Print detected conventions to terminal
patchwork show

# Auto-watch mode (regenerate on change)
patchwork watch

# Start MCP server
patchwork serve --stdio    # for Claude Code
patchwork serve --port 3742  # HTTP mode

Claude Code integration

patchwork scan      # run once
# CONVENTIONS.md is automatically read by Claude Code

Option 2: Append to CLAUDE.md

patchwork scan --claude-md

Option 3: MCP server

Claude Code — add to ~/.claude.json (or run claude mcp add interactively):

{
  "mcpServers": {
    "patchwork": {
      "command": "patchwork",
      "args": ["serve", "/path/to/your/project", "--stdio"]
    }
  }
}

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "patchwork": {
      "command": "patchwork",
      "args": ["serve", "/path/to/your/project", "--stdio"]
    }
  }
}

Cursor — add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "patchwork": {
      "command": "patchwork",
      "args": ["serve", ".", "--stdio"]
    }
  }
}

Then your AI agent can use 8 on-demand tools:

Tool

When to use

patchwork_scan

Get complete conventions overview

patchwork_naming

Before writing new identifiers

patchwork_structure

Before creating new files/directories

patchwork_stack

When choosing libraries or commands

patchwork_errors

Before writing error handling

patchwork_testing

Before writing test files

patchwork_git

Before writing commit messages

patchwork_check

Validate a proposed name

Option 4: Claude Code skill (SKILL.md)

Copy SKILL.md from this repo to ~/.claude/skills/patchwork/SKILL.md to get /patchwork slash commands.


Watch mode (CI/auto-update)

# Keep CONVENTIONS.md updated as you code
patchwork watch &

# Or in CI — fail if conventions changed
patchwork diff || (patchwork update && git add CONVENTIONS.md && git commit -m "chore: update conventions")

Python API

from patchwork import scan
from patchwork.scanner import ScanOptions
from pathlib import Path

# Full scan
report = scan(ScanOptions(root=Path(".")))

# Render to markdown
print(report.to_markdown())

# Render to JSON
import json
data = json.loads(report.to_json())

# Access specific results
naming = report.naming.get("python")
print(f"Functions: {naming.functions.style} ({naming.functions.confidence:.0%})")
print(f"Examples: {naming.functions.examples}")

structure = report.structure
print(f"Source root: {structure.source_root}")
print(f"Organisation: {structure.organisation}")

Supported languages

Language

AST (tree-sitter)

Fallback regex

Python

✅ full

TypeScript

✅ full

JavaScript

✅ full

Go

✅ (with full extra)

Rust

✅ (with full extra)

Java

✅ (with full extra)

Ruby, PHP, C#, C++

✅ regex only

Install full language support:

pip install 'patchwork-conventions[full]'

How it works

your codebase
     │
     ▼
ConfigDetector        ← reads package.json, pyproject.toml, go.mod, Cargo.toml
     │
     ▼
File discovery        ← respects .gitignore, skips node_modules etc.
     │
     ▼
Per-language AST      ← tree-sitter parses every file into a syntax tree
     │
     ├── NamingMiner       → extracts function/class/variable names, classifies style
     ├── ImportMiner        → detects import patterns, aliases, barrel files
     ├── StructureMiner     → analyses directory layout, test co-location
     ├── ErrorHandlingMiner → detects try/catch patterns, logging, custom exceptions
     ├── TestingMiner       → identifies framework, assertion style, mocking
     ├── APIPatternMiner    → finds response shapes, ORMs, route styles
     └── GitPatternMiner    → mines commit history, branches, co-change pairs
          │
          ▼
     ConventionReport
          │
          ├── CONVENTIONS.md  (default)
          ├── AGENTS.md       (--agents-md)
          ├── CLAUDE.md       (--claude-md, appends)
          └── JSON            (--json)

All analysis is 100% local — no API calls, no telemetry, no data leaves your machine.


Performance

On a 1,000-file TypeScript monorepo:

  • Without tree-sitter: ~0.8s

  • With tree-sitter (full AST): ~2.1s

On a 500-file Python project:

  • ~1.1s

Results are deterministic — same codebase always produces the same output.


Contributing

git clone https://github.com/yourusername/patchwork
cd patchwork
pip install -e '.[dev]'
pytest

Pull requests welcome. See CONTRIBUTING.md.


License

MIT — free for personal and commercial use.


Topics

claude-code · agent-skills · mcp · context-engineering · hallucination-detection · code-conventions · static-analysis · tree-sitter · developer-tools · ai-coding

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