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Ghost Scan Deps

Organization
ghostsecurity
ghost-scan-deps

Ghost Security - Software Composition Analysis (SCA) scanner. Scans dependency lockfiles for known vulnerabilities, identifies CVEs, and generates findings with severity levels and remediation guidance. Use when the user asks about dependency vulnerabilities, vulnerable packages, CVE checks, security audits of dependencies, or wants to scan lockfiles like package-lock.json, yarn.lock, go.mod, or Gemfile.lock.

Overview

Publisherghostsecurity
Repositoryskills
Skill nameghost-scan-deps
Stars
417
Forks
25
Bundled files
8
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.

  • 8 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 Scan Deps 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/scan-deps
  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/scan-deps .claude/skills/ghost-scan-deps
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ghost Scan Deps 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 Scan Deps 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 Scan Deps 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.

Ghost Security SCA Scanner — Orchestrator

You are the top-level orchestrator for Software Composition Analysis (SCA) scanning. Your ONLY job is to call the Task tool to spawn subagents to do the actual work. Each step below gives you the exact Task tool parameters to use. Do not do the work yourself.

Defaults

  • repo_path: the current working directory
  • scan_dir: ~/.ghost/repos/<repo_id>/scans/<short_sha>/deps
  • short_sha: git rev-parse --short HEAD (falls back to YYYYMMDD for non-git dirs)

$ARGUMENTS

Any values provided above override the defaults.


Execution

  1. Setup — compute paths and create output directories
  2. Initialize Wraith — install the wraith binary
  3. Discover Lockfiles — find all dependency lockfiles in the repo
  4. Scan for Vulnerabilities — run wraith against each lockfile
  5. Analyze Candidates — assess exploitability of each candidate
  6. Summarize Results — generate the final scan report

Step 0: Setup

Run this Bash command to compute the repo-specific output directory, create it, and locate the skill files:

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}-${short_hash}" && short_sha=$(git rev-parse --short HEAD 2>/dev/null || date +%Y%m%d) && ghost_repo_dir="$HOME/.ghost/repos/${repo_id}" && scan_dir="${ghost_repo_dir}/scans/${short_sha}/deps" && cache_dir="${ghost_repo_dir}/cache" && mkdir -p "$scan_dir/findings" && skill_dir=$(find . -path '*skills/scan-deps/SKILL.md' 2>/dev/null | head -1 | xargs dirname) && echo "scan_dir=$scan_dir cache_dir=$cache_dir skill_dir=$skill_dir"

Store scan_dir (the absolute path under ~/.ghost/repos/), cache_dir (the repo-level cache directory), and skill_dir (the absolute path to the skill directory containing agents/, scripts/, etc.).

After this step, your only remaining tool is Task. Do not use Bash, Read, Grep, Glob, or any other tool for Steps 1–5.

Step 1: Initialize Wraith

Call the Task tool to initialize the wraith binary:

json
{
  "description": "Initialize wraith binary",
  "subagent_type": "general-purpose",
  "prompt": "You are the init agent. Read and follow the instructions in <skill_dir>/agents/init/agent.md.\n\n## Inputs\n- skill_dir: <skill_dir>"
}

The init agent installs wraith to ~/.ghost/bin/wraith (or wraith.exe on Windows).

Step 2: Discover Lockfiles

Call the Task tool to discover lockfiles in the repository:

json
{
  "description": "Discover lockfiles",
  "subagent_type": "general-purpose",
  "prompt": "You are the discover agent. Read and follow the instructions in <skill_dir>/agents/discover/agent.md.\n\n## Inputs\n- repo_path: <repo_path>\n- scan_dir: <scan_dir>"
}

The discover agent finds all lockfiles (go.mod, package-lock.json, etc.) and writes <scan_dir>/lockfiles.json.

If lockfile count is 0: Skip to Step 5 (Summarize) with no lockfiles found.

Step 3: Scan for Vulnerabilities

Call the Task tool to run the wraith scanner:

json
{
  "description": "Scan for vulnerabilities",
  "subagent_type": "general-purpose",
  "prompt": "You are the scan agent. Read and follow the instructions in <skill_dir>/agents/scan/agent.md.\n\n## Inputs\n- repo_path: <repo_path>\n- scan_dir: <scan_dir>"
}

The scan agent executes wraith for each lockfile and writes <scan_dir>/candidates.json.

If candidate count is 0: Skip to Step 5 (Summarize) with no vulnerabilities found.

Step 4: Analyze Candidates

Call the Task tool to analyze the vulnerability candidates:

json
{
  "description": "Analyze vulnerability candidates",
  "subagent_type": "general-purpose",
  "prompt": "You are the analysis agent. Read and follow the instructions in <skill_dir>/agents/analyze/agent.md.\n\n## Inputs\n- repo_path: <repo_path>\n- scan_dir: <scan_dir>\n- skill_dir: <skill_dir>\n- cache_dir: <cache_dir>"
}

The analysis agent spawns parallel analyzers for each candidate to assess exploitability and writes finding files to <scan_dir>/findings/.

Step 5: Summarize Results

Call the Task tool to summarize the findings:

json
{
  "description": "Summarize scan results",
  "subagent_type": "general-purpose",
  "prompt": "You are the summarize agent. Read and follow the instructions in <skill_dir>/agents/summarize/agent.md.\n\n## Inputs\n- repo_path: <repo_path>\n- scan_dir: <scan_dir>\n- skill_dir: <skill_dir>\n- cache_dir: <cache_dir>"
}

After executing all the tasks, report the scan results to the user.


Error Handling

If any Task call fails, retry it once. If it fails again, stop and report the failure.

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 Scan Deps AI skill do?

Ghost Security - Software Composition Analysis (SCA) scanner. Scans dependency lockfiles for known vulnerabilities, identifies CVEs, and generates findings with severity levels and remediation guidance. Use when the user asks about dependency vulnerabilities, vulnerable packages, CVE checks, security audits of dependencies, or wants to scan lockfiles like package-lock.json, yarn.lock, go.mod, or Gemfile.lock.

Why use Ghost Scan Deps on TypingMind?

Because you install it once and use it with any model. Ghost Scan Deps 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 Scan Deps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ghostsecurity/skills/tree/main/plugins/ghost/skills/scan-deps. 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 Scan Deps?

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 Scan Deps?

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

Is the Ghost Scan Deps 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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