Commit Security Scan logo

Commit Security Scan

Organization
Factory-AI
commit-security-scan

Analyze code changes for security vulnerabilities using LLM reasoning and threat model patterns. Use for PR reviews, pre-commit checks, or branch comparisons.

Overview

PublisherFactory-AI
Repositoryfactory-plugins
Skill namecommit-security-scan
Stars
111
Forks
15
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by Factory-AI on GitHub. Read the source before you install it.

Installation

Install the Commit Security Scan 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/Factory-AI/factory-plugins.git /tmp/factory-plugins
mkdir -p .claude/skills
cp -r /tmp/factory-plugins/plugins/security-engineer/skills/commit-security-scan .claude/skills/commit-security-scan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Commit Security Scan 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 Commit Security Scan 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 Commit Security Scan 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.

Commit Security Scan

Analyze code changes (commits, PRs, diffs) using LLM-powered reasoning to detect security vulnerabilities. This skill reads code directly and applies patterns from the repository's threat model to identify issues across all STRIDE categories.

When to Use This Skill

  • PR review - Automated security scan on pull requests
  • Pre-commit check - Scan staged changes before committing
  • Branch comparison - Review security of feature branch changes
  • Code review assistance - Help reviewers spot security issues

Prerequisites

This skill requires:

  1. Threat model - .factory/threat-model.md must exist
  2. Security config - .factory/security-config.json for severity thresholds

IMPORTANT: If these files don't exist, you MUST generate them first before proceeding with the security scan.

To generate the prerequisites:

  1. Tell the user: "The threat model doesn't exist yet. I'll generate it first before scanning."
  2. Run the threat-model-generation skill to create both files
  3. Once complete, continue with the security scan

Do NOT ask the user to run the skill manually - just do it automatically as part of this workflow.

Inputs

The skill determines what to scan from the user's request:

Scan TypeHow to SpecifyExample
PR"Scan PR #123"Scan PR #456 for security vulnerabilities
Commit range"Scan commits X..Y"Scan commits abc123..def456
Single commit"Scan commit X"Scan commit abc123
Staged changes"Scan staged changes"Scan my staged changes for security issues
Uncommitted"Scan uncommitted changes"Scan working directory changes
Branch comparison"Scan from X to Y"Scan changes from main to feature-branch
Last N commits"Scan last N commits"Scan the last 3 commits

If no scope is specified, prompt the user for clarification.

Instructions

Follow these steps in order:

Step 1: Verify Prerequisites (Auto-Generate if Missing)

Try to read these files:

  • .factory/threat-model.md
  • .factory/security-config.json

If either file is missing or cannot be read:

  1. Inform the user: "The security threat model doesn't exist yet. I'll generate it first - this may take a minute."
  2. Invoke the threat-model-generation skill to analyze the repository and create both files
  3. Once generation completes, continue with Step 2

This ensures the security scan always has the threat model context it needs for accurate analysis.

Step 2: Get Changed Files

Based on the user's request, get the list of changed files and their diffs using git:

  • For PRs: use gh pr diff
  • For commits/ranges: use git diff or git show
  • For staged changes: use git diff --cached

Read the full content of each changed file for context.

Step 3: Load Threat Model

Read .factory/threat-model.md and .factory/security-config.json to understand:

  • The system's architecture and trust boundaries
  • Known vulnerability patterns for this codebase
  • Severity thresholds for findings

Step 4: Analyze for Vulnerabilities

For each changed file, systematically check for STRIDE threats:

S - Spoofing Identity
  • Missing or weak authentication checks
  • Session handling vulnerabilities
  • Token/credential exposure in code
  • Insecure cookie settings
T - Tampering with Data
  • SQL Injection: String concatenation/interpolation in SQL queries
  • Command Injection: User input in shell commands, eval(), exec()
  • XSS: Unescaped user input in HTML/templates
  • Mass Assignment: Blind assignment from request to model
  • Path Traversal: User input in file paths without validation
R - Repudiation
  • Missing audit logging for sensitive operations
  • Insufficient error logging
  • Log injection vulnerabilities
I - Information Disclosure
  • IDOR: Direct object access without ownership verification
  • Verbose error messages exposing internals
  • Hardcoded secrets, API keys, credentials
  • Sensitive data in logs or responses
  • Debug endpoints exposed
D - Denial of Service
  • Missing rate limiting on endpoints
  • Unbounded resource consumption (file uploads, queries)
  • Algorithmic complexity attacks (regex, sorting)
  • Missing pagination on list endpoints
E - Elevation of Privilege
  • Missing authorization checks on endpoints
  • Role/permission bypass opportunities
  • Privilege escalation through parameter manipulation

Step 5: Assess Each Finding

For each potential vulnerability:

  1. Trace data flow: Follow user input from source to sink

    • Where does the input come from? (request params, body, headers, files)
    • Does it pass through validation/sanitization?
    • Where does it end up? (database, shell, response, file system)
  2. Check for existing mitigations:

    • Is there validation elsewhere in the codebase?
    • Are there middleware/decorators that protect this code?
    • Does the framework provide automatic protection?
  3. Determine severity:

    • CRITICAL: Remote code execution, auth bypass, data breach
    • HIGH: SQL injection, XSS, IDOR, privilege escalation
    • MEDIUM: Information disclosure, missing security headers
    • LOW: Best practice violations, minor issues
  4. Assess confidence:

    • HIGH: Clear vulnerable pattern, direct data flow, no mitigations
    • MEDIUM: Possible vulnerability, some uncertainty about context
    • LOW: Suspicious pattern, likely has mitigations we can't see

Step 6: Generate Report

Create security-findings.json with this structure:

json
{
  "scan_id": "scan-YYYY-MM-DD-XXX",
  "scan_date": "<ISO 8601 timestamp>",
  "scan_type": "pr|commit|range|staged|working",
  "commit_range": "<base>..<head>",
  "pr_number": null,
  "threat_model_version": "<from security-config.json>",
  "findings": [
    {
      "id": "VULN-001",
      "severity": "HIGH",
      "stride_category": "Tampering",
      "vulnerability_type": "SQL Injection",
      "cwe": "CWE-89",
      "file": "src/api/users.py",
      "line_range": "45-49",
      "code_context": "<vulnerable code snippet>",
      "analysis": "<explanation of why this is vulnerable>",
      "exploit_scenario": "<how an attacker could exploit this>",
      "threat_model_reference": "Section 5.2 - SQL Injection",
      "existing_mitigations": [],
      "recommended_fix": "<how to fix the vulnerability>",
      "confidence": "HIGH",
      "reasoning": "<why this confidence level>"
    }
  ],
  "summary": {
    "total_findings": 0,
    "by_severity": { "CRITICAL": 0, "HIGH": 0, "MEDIUM": 0, "LOW": 0 },
    "by_stride": {
      "Spoofing": 0,
      "Tampering": 0,
      "Repudiation": 0,
      "InfoDisclosure": 0,
      "DoS": 0,
      "ElevationOfPrivilege": 0
    },
    "files_analyzed": 0
  }
}

Step 7: Report Results

  1. Save findings to security-findings.json
  2. Report summary to user (findings count by severity, triggered thresholds)
  3. Check severity thresholds from security-config.json and note if any are triggered

CWE Reference

Common CWE mappings for findings:

Vulnerability TypeCWE
SQL InjectionCWE-89
Command InjectionCWE-78
XSS (Reflected)CWE-79
XSS (Stored)CWE-79
Path TraversalCWE-22
IDORCWE-639
Missing AuthenticationCWE-306
Missing AuthorizationCWE-862
Hardcoded CredentialsCWE-798
Sensitive Data ExposureCWE-200
Mass AssignmentCWE-915
Open RedirectCWE-601
SSRFCWE-918
XXECWE-611
Insecure DeserializationCWE-502

Example Invocations

Scan a PR:

Scan PR #123 for security vulnerabilities

Scan staged changes before committing:

Scan my staged changes for security issues

Scan a feature branch:

Scan changes from main to feature/user-auth for vulnerabilities

Scan recent commits:

Scan the last 5 commits for security issues

References

  • Analysis examples: analysis-examples.md (in this skill directory)
  • Threat model: .factory/threat-model.md
  • Security config: .factory/security-config.json
  • OWASP Top 10
  • CWE Top 25

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 Commit Security Scan AI skill do?

Analyze code changes for security vulnerabilities using LLM reasoning and threat model patterns. Use for PR reviews, pre-commit checks, or branch comparisons.

Why use Commit Security Scan on TypingMind?

Because you install it once and use it with any model. Commit Security Scan 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 Commit Security Scan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Factory-AI/factory-plugins/tree/master/plugins/security-engineer/skills/commit-security-scan. 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 Commit Security Scan?

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 Commit Security Scan?

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

Is the Commit Security Scan AI skill free?

It is published on GitHub by Factory-AI. Check the repository for licensing terms. 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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