cq

Identify Python and Rust code patterns and provide Markdown-formatted analysis for repository discovery.

Updated Jan 10, 2026
One-click install
npx skills add https://github.com/paul-heyse/CodeAnatomy --skill cq
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: cq
Source: https://github.com/paul-heyse/CodeAnatomy/tree/main/.claude/skills/cq
Command: npx skills add https://github.com/paul-heyse/CodeAnatomy --skill cq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

High-signal code analysis for Python and Rust that surfaces actionable findings and enables rapid repository understanding before making changes.

Core Features & Use Cases

  • Semantic code discovery with integrated searches, AST-based enrichment, and cross-language results for Python and Rust.
  • Neighborhood analysis and LDMD progressive disclosure to inspect symbol neighborhoods and surface structured context.
  • Multi-step workflows (run/chain) to orchestrate scans, plan changes, and track telemetry across tasks.
  • Rust and Python language support with LSP-backed enrichment planes and robust cross-language summaries.

Quick Start

Ask CQ to analyze a repository for a target symbol and inspect its semantic neighborhood.

Frequently Asked Questions about cq

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze Python and Rust code patterns for repository discovery?

You can perform semantic code analysis for Python and Rust by identifying code patterns and generating Markdown-formatted results. This provides actionable findings for rapid repository understanding before making refactoring changes.

What is neighborhood analysis in semantic code discovery?

Neighborhood analysis inspects symbol neighborhoods to surface structured context around target code. It uses LDMD progressive disclosure to provide high-signal, cross-language results for Python and Rust repositories.

Can I orchestrate multi-step code scans to plan repository changes?

Yes, you can run and chain multi-step workflows to orchestrate scans, plan changes, and track telemetry across tasks. This allows you to sequence repository discovery operations before executing refactoring decisions.

Does this code analysis approach support both Python and Rust enrichment?

Yes, Rust and Python language support includes LSP-backed enrichment planes and AST-based enrichment. This provides robust cross-language summaries and integrated search results for both programming languages.

When do I need structured enrichment contracts for code analysis?

Structured enrichment contracts are needed when you require LDMD-enriched outputs to support refactoring decisions. They provide progressive disclosure of symbol neighborhoods to deliver high-signal context during repository discovery.

What is the best way to inspect a semantic neighborhood for a target symbol?

The best way to inspect a semantic neighborhood is to run a targeted scan for the symbol and use LDMD progressive disclosure. This surfaces structured, Markdown-formatted context to support rapid repository understanding.