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Ghost Repo Context

Organization
ghostsecurity
ghost-repo-context

Scans directory structure, detects projects, maps dependencies, and documents code organization into a repo.md file. Use when the user needs a codebase overview, project structure map, or repository context before security analysis.

Overview

Publisherghostsecurity
Repositoryskills
Skill nameghost-repo-context
Stars
417
Forks
25
Bundled files
3
Licenseapache-2.0
Links
  • 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 ghostsecurity on GitHub. Read the source before you install it.

Installation

Install the Ghost Repo Context 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.
    https://github.com/ghostsecurity/skills/tree/main/plugins/ghost/skills/repo-context
  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/ghostsecurity/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/ghost/skills/repo-context .claude/skills/ghost-repo-context
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ghost Repo Context 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 Ghost Repo Context 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 Ghost Repo Context 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.

Repository Context Builder

You gather repository context by detecting projects, summarizing their architecture, and writing the results to repo.md. Do all work yourself — do not spawn subagents or delegate.

Inputs

Parse these from $ARGUMENTS (key=value pairs):

  • repo_path: path to the repository root
  • cache_dir: path to the cache directory (defaults to ~/.ghost/repos/<repo_id>/cache)

$ARGUMENTS

If cache_dir is not provided, compute it:

bash
repo_name=$(basename "$(pwd)") && remote_url=$(git remote get-url origin 2>/dev/null || pwd) && short_hash=$(printf '%s' "$remote_url" | git hash-object --stdin | cut -c1-8) && repo_id="${repo_name}-${short_hash}" && cache_dir="$HOME/.ghost/repos/${repo_id}/cache" && echo "cache_dir=$cache_dir"

Tool Restrictions

Do NOT use WebFetch or WebSearch. All work must use only local files in the repository.

Setup

Discover this skill's own directory so you can reference agent files:

bash
skill_dir=$(find . -path '*/skills/repo-context/SKILL.md' 2>/dev/null | head -1 | xargs dirname)
echo "skill_dir=$skill_dir"

Check Cache First

Check if <cache_dir>/repo.md already exists. If it does, skip everything and return:

Repository context is at: <cache_dir>/repo.md

If it does not exist, run mkdir -p <cache_dir> and continue.


Workflow

  1. Detect Projects — Read <skill_dir>/detector.md and follow its instructions against <repo_path>. Save the full detection output (project details needed for step 2). If detection finds no projects, write a minimal repo.md noting "No projects detected" and skip to step 4.

  2. Summarize Each Project — Read <skill_dir>/summarizer.md. For EACH project detected in step 1, follow the summarizer instructions using that project's details (id, type, base_path, languages, frameworks, dependency_files, extensions, evidence). Collect the summary for each project. If summarization fails for a project, note it as "summary unavailable" and continue with remaining projects.

  3. Write repo.md — Combine detection and summary results into <cache_dir>/repo.md using the format in <skill_dir>/template-repo.md. For each project include:

    • Detection: ID, Type, Base Path, Languages, Frameworks, Dependency Files, Extensions, Evidence
    • Summary: Architectural summary, Sensitive Data Types, Business Criticality, Component Map, Evidence
  4. Validate — Read <cache_dir>/repo.md back and verify it contains the expected sections from <skill_dir>/template-repo.md (e.g., project entries with Detection and Summary fields). If the file is missing or malformed, retry the write once before reporting an error.

  5. Output — Return: Repository context is at: <cache_dir>/repo.md

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 Ghost Repo Context AI skill do?

Scans directory structure, detects projects, maps dependencies, and documents code organization into a repo.md file. Use when the user needs a codebase overview, project structure map, or repository context before security analysis.

Why use Ghost Repo Context on TypingMind?

Because you install it once and use it with any model. Ghost Repo Context 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 Ghost Repo Context in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ghostsecurity/skills/tree/main/plugins/ghost/skills/repo-context. 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 Ghost Repo Context?

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 Ghost Repo Context?

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

Is the Ghost Repo Context AI skill free?

Yes. It is published on GitHub by ghostsecurity under the apache-2.0 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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