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JeanFrancoisGagne/crapkit

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Per-function CRAP scores from complexity and coverage, churn-ranked worklists, and ratchet gates for code changes. CLI, MCP, and GitHub Action.

MCP

JeanFrancoisGagne/crapkit

Added 16 Sept 2026

#claude-code #code-quality #coverage #cyclomatic-complexity #mcp #pre-commit #python #static-analysis

Overview

Computes per-function CRAP scores from code complexity and test coverage. Provides churn-ranked worklists and ratchet gates for enforcing quality thresholds. Available as a CLI, MCP server, and GitHub Action.

Best for

Best for
Python developers who want to gate code changes on CRAP score trends

Use cases

  • Identify functions with high complexity and low coverage
  • Prioritize refactoring by churn frequency
  • Enforce coverage and complexity gates in CI via GitHub Action

How to use

Install

pip install crapkit

Tools exposed

  • Objective-C
  • python-version

Tested with

Claude Code

Notes

Computes per-function CRAP scores from code complexity and test coverage. Provides churn-ranked worklists and ratchet gates for enforcing quality thresholds. Available as a CLI, MCP server, and GitHub Action.

1 stars on GitHub. Last updated 2026-09-16. Licensed MIT.

Use cases

  • Identify functions with high complexity and low coverage
  • Prioritize refactoring by churn frequency
  • Enforce coverage and complexity gates in CI via GitHub Action

Pros

  • Combines complexity and coverage into a single score
  • Offers multiple integration points: CLI, MCP, and GitHub Action
  • Focuses on per-function granularity for targeted improvements

Cons

  • Limited adoption (1 star) suggests an immature ecosystem
  • Python-only, not language-agnostic
  • Requires existing coverage data to compute scores

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Combines complexity and coverage into a single score
  • Offers multiple integration points: CLI, MCP, and GitHub Action
  • Focuses on per-function granularity for targeted improvements

Cons

  • Limited adoption (1 star) suggests an immature ecosystem
  • Python-only, not language-agnostic
  • Requires existing coverage data to compute scores
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