Repo Audit logo

Repo Audit

CommunityPopular
zebbern
repo-audit

Deep analysis of Git history: identify frequently changed hotspot files, analyze code ownership by contributor, and scan for leaked secrets. Triggered when users ask about Git analysis, code hotspots, who owns what code, secret scanning, security audits of commit history, or optimizing code review assignments.

Overview

Publisherzebbern
Repositoryclaude-code-guide
Skill namerepo-audit
Stars
4.6K
Forks
464
Bundled files
3
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zebbern on GitHub. Read the source before you install it.

Installation

Install the Repo Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zebbern/claude-code-guide.git /tmp/claude-code-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-guide/skills/repo-audit .claude/skills/repo-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Repo Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Repo Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Repo Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Repo Audit — Deep Analysis of Git History

Perform three-dimensional analysis on a Git repository: hotspot file detection, code ownership analysis, and secret leak scanning.

Feature Overview

1. Hotspot File Analysis (scripts/hotfiles.sh)

Identify the most frequently changed files in a repository to help spot:

  • High-risk code areas (frequent changes = potential instability)
  • Files that deserve extra attention during code review
  • Modules that may need splitting or refactoring

Usage:

bash
bash scripts/hotfiles.sh [options]
OptionDescriptionDefault
--repo PATHRepository pathCurrent directory
--top NShow top N files20
--since DATEStart date (e.g. 2024-01-01)None
--until DATEEnd dateNone
--author AUTHORFilter by authorNone
--format FORMATOutput format: table / csv / jsontable

2. Code Ownership Analysis (scripts/ownership.sh)

Analyze actual code ownership, reporting for each contributor within the specified scope:

  • Commit count and percentage
  • Lines changed (additions/deletions)
  • Last active date

Usage:

bash
bash scripts/ownership.sh [options]
OptionDescriptionDefault
--repo PATHRepository pathCurrent directory
--path SUBPATHAnalyze a specific subdirectory or fileEntire repo
--top NShow top N contributors10
--since DATEStart dateNone
--format FORMATOutput format: table / csv / jsontable

3. Secret Leak Scanning (scripts/secret-scan.sh)

Scan the full Git history (including deleted commits) for common secrets and sensitive information:

  • AWS Access Key / Secret Key
  • GitHub / GitLab / Slack Tokens
  • SSH Private Keys
  • Generic API Keys, passwords, and secret patterns

Usage:

bash
bash scripts/secret-scan.sh [options]
OptionDescriptionDefault
--repo PATHRepository pathCurrent directory
--branch BRANCHScan a specific branchAll branches
--since DATEStart dateNone
--format FORMATOutput format: table / csv / jsontable
--severity LEVELMinimum severity level: low / medium / highlow

Use Cases

  • Security audits: Scan history for leaked secrets before deploying to production
  • Code review optimization: Identify hotspot files and prioritize reviewing high-risk areas
  • Team collaboration: Understand who knows which parts of the code best, and assign reviews accordingly
  • Tech debt assessment: Frequently changed files are strong candidates for refactoring

Dependencies

  • git (>= 2.20)
  • bash (>= 4.0)
  • Standard Unix utilities: awk, sort, head, grep

No additional dependencies or paid APIs required.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Repo Audit AI skill do?

Deep analysis of Git history: identify frequently changed hotspot files, analyze code ownership by contributor, and scan for leaked secrets. Triggered when users ask about Git analysis, code hotspots, who owns what code, secret scanning, security audits of commit history, or optimizing code review assignments.

Why use Repo Audit on TypingMind?

Because you install it once and use it with any model. Repo Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Repo Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zebbern/claude-code-guide/tree/main/skills/repo-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Repo Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Repo Audit?

As many as you like. As long as a model supports skills, you can use Repo Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Repo Audit AI skill free?

Yes. It is published on GitHub by zebbern under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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