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get-tmonier/argot

by Various

A statistical code analyzer built from your repository's history.

MCP

get-tmonier/argot

Added 3 Sept 2026

#agents #ai #ai-code-review #ai-guardrails #ci #claude #cli #code-quality

Overview

Argot is a statistical code analyzer that derives insights from your repository's commit history. Written in Rust, it processes version control data to produce metrics about code evolution and developer activity. It aims to help developers understand their codebase's health and trends.

Best for

Best for
Developers seeking quantitative insights from their version control history.

Use cases

  • Analyze commit history for code churn patterns
  • Identify files with frequent modifications
  • Track developer contribution trends over time

How to use

Tools exposed

  • foreign-import
  • unfamiliar-callee
  • rare-tokens
  • test-deleted
  • test-disabled
  • test-weakened
  • rule-tampered
  • pre-commit

Tested with

Claude Code

Notes

Argot is a statistical code analyzer that derives insights from your repository’s commit history. Written in Rust, it processes version control data to produce metrics about code evolution and developer activity. It aims to help developers understand their codebase’s health and trends.

48 stars on GitHub. Last updated 2026-09-02. Licensed MIT.

Use cases

  • Analyze commit history for code churn patterns
  • Identify files with frequent modifications
  • Track developer contribution trends over time

Pros

  • Fast performance due to Rust implementation
  • Open source with a simple command-line interface
  • Uses existing repository data, no extra setup

Cons

  • Small community and limited documentation
  • Focuses on statistical metrics, not code quality analysis
  • Requires a git-based repository history

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

Pros

  • Fast performance due to Rust implementation
  • Open source with a simple command-line interface
  • Uses existing repository data, no extra setup

Cons

  • Small community and limited documentation
  • Focuses on statistical metrics, not code quality analysis
  • Requires a git-based repository history
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