codebase-digest: Your AI-friendly codebase packer and analyzer

by kamilstanuch · Claude Skill · ★ 397

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install codebase-digest

pip install codebase-digest
git clone https://github.com/kamilstanuch/codebase-digest.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 72/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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Quick Facts

Stars397
Forks37
LanguagePython
CategoryClaude Skill
Quality Score72/100
Open Issues5
Last Updated2024-10-21
Created2024-09-07
Platformsclaude-code, gemini, python
Est. Tokens~98k

About codebase-digest

Codebase Digest Codebase Digest is a command-line tool written in Python that helps you analyze and understand your codebase. It provides a structured overview of your project's directory structure, file sizes, token counts, and even consolidates the content of all text-based files into a single output for easy analysis with Large Language Models (LLMs).

Compatible Skills

These tools work well together with codebase-digest for enhanced workflows:

  • claude-gemini-mcp-slim — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (52%)
  • wcgw — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • claude_code-gemini-mcp — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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