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Ghost Scan Code

Organization
ghostsecurity
ghost-scan-code

Ghost Security - SAST code scanner. Finds security vulnerabilities in source code by planning and executing targeted scans for issues like SQL injection, XSS, BOLA, BFLA, SSRF, and other OWASP categories. Supports applications (backend, frontend, mobile) and libraries (prototype pollution, unsafe deserialization, ReDoS, path traversal, zip slip). Use when the user asks for a code security audit, SAST scan, vulnerability scan of source code, or wants to find security flaws in a codebase or library.

Overview

Publisherghostsecurity
Repositoryskills
Skill nameghost-scan-code
Stars
417
Forks
25
Bundled files
11
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.

  • 11 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 Code 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-code
  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-code .claude/skills/ghost-scan-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ghost Scan Code 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 Code 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 Code 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.

Find Issues

You find security issues in a repository. This skill plans which vulnerability vectors to scan, then executes those scans against each project.

Inputs

  • depth: quick (default), balanced, or full — override via $ARGUMENTS

$ARGUMENTS

Note: Arguments passed can be used to customize the scan workflow if provided. For example, if the user specifies a specific set of vectors, count of vectors, specific candidate files, areas to focus on, count of candidate files, etc., ensure the relevant details are passed to the relevant steps in the skill.

Supporting files


Step 1: Setup

Compute the repo-specific output directory:

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}-${short_hash}" && short_sha=$(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}/code" && cache_dir="${ghost_repo_dir}/cache" && mkdir -p "$scan_dir" && echo "scan_dir=$scan_dir cache_dir=$cache_dir"
  1. Read $cache_dir/repo.md — if missing, run the repo-context skill first and then continue.
  2. Read criteria/index.yaml to get the valid agent→vector mappings per project type
  3. Set depth to quick if not provided
  4. If depth is full, warn the user that a full scan uses significantly more tokens and ask them to confirm before proceeding. If they decline, fall back to balanced.

Step 2: Plan Scans

If $scan_dir/plan.md already exists, skip to the next step.

Otherwise, run the planner using scripts/loop.sh:

bash
bash <path-to-loop.sh> $scan_dir planner.md "- depth: <depth>
- arguments: <relevant argument overrides if any, otherwise omit>" 1 $cache_dir

Use a 10-minute timeout. If the command times out, re-run it — the script resumes from where it left off. If it fails 3 times consecutively with the same error, stop and report the failure.

Verify: $scan_dir/plan.md exists and contains at least one ## Project: section before proceeding.


Step 3: Nominate Files

If $scan_dir/nominations.md does not exist, generate it by reading $scan_dir/plan.md and for each project section (## Project: <base_path> (<type>)), parse the Recommended Scans table. For each row, extract the Agent and Vector columns. Write $scan_dir/nominations.md - one line per (project, agent, vector) combination. Skip projects with empty scan tables.

markdown
# Nominations

- [ ] <base_path> (<type>) | <agent> | <vector>
- [ ] <base_path> (<type>) | <agent> | <vector>
...

If $scan_dir/nominations.md already exists, change every top level task - [x] to - [ ]. Keep all indented lines/subtasks beneath each item unchanged.

Run nomination script

Using scripts/loop.sh:

bash
bash <path-to-loop.sh> $scan_dir nominator.md "- depth: <depth>
- arguments: <relevant argument overrides if any, otherwise omit>" 5 $cache_dir

Use a 10-minute timeout. If the command times out, re-run it — the script resumes from where it left off. If it fails 3 times consecutively with the same error, stop and report the failure.

Verify: $scan_dir/nominations.md contains at least one - [x] line before proceeding.


Step 4: Analyze Nominated Files

Read $scan_dir/nominations.md. For each candidate file under a checked - [x] line, append to $scan_dir/analyses.md (skip candidates already listed in analyses.md).

- [ ] <base_path> (<type>) | <agent> | <vector> | <candidate_file>

Create the findings directory:

bash
mkdir -p $scan_dir/findings

Run analysis script

Using scripts/loop.sh:

bash
bash <path-to-loop.sh> $scan_dir analyzer.md "" 5 $cache_dir

Use a 10-minute timeout. If the command times out, re-run it — the script resumes from where it left off. If it fails 3 times consecutively with the same error, stop and report the failure.

Verify: $scan_dir/analyses.md contains at least one - [x] line before proceeding.


Step 5: Verify Findings

List all .md files in $scan_dir/findings/. If none exist, write a no-findings.md summary and stop.

Using scripts/loop.sh:

bash
bash <path-to-loop.sh> $scan_dir verifier.md "" 5 $cache_dir

Use a 10-minute timeout. If the command times out, re-run it — the script resumes from where it left off. If it fails 3 times consecutively with the same error, stop and report the failure.


Completion

After all steps complete, report the scan results:

  1. List all finding files in $scan_dir/findings/
  2. Count verified vs rejected findings
  3. Present a summary to the user

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 Code AI skill do?

Ghost Security - SAST code scanner. Finds security vulnerabilities in source code by planning and executing targeted scans for issues like SQL injection, XSS, BOLA, BFLA, SSRF, and other OWASP categories. Supports applications (backend, frontend, mobile) and libraries (prototype pollution, unsafe deserialization, ReDoS, path traversal, zip slip). Use when the user asks for a code security audit, SAST scan, vulnerability scan of source code, or wants to find security flaws in a codebase or library.

Why use Ghost Scan Code on TypingMind?

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

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

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 Code?

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

Is the Ghost Scan Code 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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