skills.rest
BradSegal
BradSegalCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

code packing & extraction (anatomize)

Generate deterministic, token-efficient code maps and review bundles for Python repositories.

1|Updated Jan 31, 2026
Search Tags:#code review#code analysis#python#llm#bundle#skeleton#pack
One-click install
npx skills add https://github.com/BradSegal/anatomize --skill code-packing-extraction-anatomize
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code packing & extraction (anatomize)
Source: https://github.com/BradSegal/anatomize/tree/main
Command: npx skills add https://github.com/BradSegal/anatomize --skill code-packing-extraction-anatomize

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of preparing codebases for review by AI models or human collaborators, ensuring that the output is compact, deterministic, and easy to process.

Core Features & Use Cases

  • Code Bundling: Create single-file artifacts (repomix-style) of a repository or specific slices.
  • Token Efficiency: Generate token-efficient representations using hybrid modes or compression.
  • Dependency Slicing: Extract code based on import closures or symbol references.
  • Use Case: Prepare a specific microservice's codebase, including its direct dependencies, into a single Markdown file for an AI to review for potential bugs or suggest improvements.

Quick Start

Use anatomize to pack the current directory into a markdown file named codebase.md.

Frequently Asked Questions about code packing & extraction (anatomize)

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

FAQPage Schema
How do I bundle Python code for AI review?▼

To bundle Python code for AI review, generate deterministic single-file review bundles using the anatomize pack command. This creates token-efficient Markdown artifacts containing your repository's code for LLM processing.

What is code skeleton generation and how does it help with LLM analysis?▼

Code skeleton generation extracts repository hierarchy, modules, and function signatures into a token-efficient map. This compact representation allows LLMs to understand project structure without processing full source files.

Can I extract specific dependencies and imports from a Python repository?▼

Yes, you can extract specific dependencies using dependency slicing via the anatomize tool. This filters and slices code based on import closures or symbol references to isolate relevant code sections.

What is the best way to compress a Python codebase for token-efficient AI input?▼

The best way to compress a Python codebase for token-efficient AI input is using anatomize's hybrid modes and compression features. This generates compact code maps optimized for LLM context windows.

Does the anatomize code packing tool support repositories other than Python?▼

The anatomize code packing tool currently focuses on Python repositories, generating skeleton hierarchies and review bundles specifically for Python codebases via CLI commands.

Related Skills

Explore More Software Engineering Skills→
vibery-studiovibery-studio
3

template-skill

Scaffold new Claude Skill units with a ready-to-fill SKILL.md skeleton.

Official
Basic
yy1588133yy1588133
2

cleanup

Detect and remove redundant and low-quality AI-generated code from source files.

Community
Intermediate
phuhao00phuhao00
12

code-analyst-agent

Analyze source code to identify symbols, call graphs, and architectural patterns.

Community
Intermediate
XinyuYao98XinyuYao98

web-artifacts-builder

Create and bundle Claude.ai HTML artifacts with React, Tailwind CSS, and shadcn/ui.

Community
Advanced