Sast Configuration logo

Sast Configuration

Organization
Ormus-Solutions
sast-configuration

Configure Static Application Security Testing (SAST) tools for automated vulnerability detection in application code. Use when setting up security scanning, implementing DevSecOps practices, or automating code vulnerability detection.

Overview

PublisherOrmus-Solutions
RepositoryLibreUIUX-Claude-Code
Skill namesast-configuration
Stars
122
Forks
17
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Ormus-Solutions on GitHub. Read the source before you install it.

Installation

Install the Sast Configuration 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/Ormus-Solutions/LibreUIUX-Claude-Code/tree/main/plugins/security-scanning/skills/sast-configuration
  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/Ormus-Solutions/LibreUIUX-Claude-Code.git /tmp/LibreUIUX-Claude-Code
mkdir -p .claude/skills
cp -r /tmp/LibreUIUX-Claude-Code/plugins/security-scanning/skills/sast-configuration .claude/skills/sast-configuration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sast Configuration 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 Sast Configuration 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 Sast Configuration 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.

SAST Configuration

Static Application Security Testing (SAST) tool setup, configuration, and custom rule creation for comprehensive security scanning across multiple programming languages.

Overview

This skill provides comprehensive guidance for setting up and configuring SAST tools including Semgrep, SonarQube, and CodeQL. Use this skill when you need to:

  • Set up SAST scanning in CI/CD pipelines
  • Create custom security rules for your codebase
  • Configure quality gates and compliance policies
  • Optimize scan performance and reduce false positives
  • Integrate multiple SAST tools for defense-in-depth

Core Capabilities

1. Semgrep Configuration

  • Custom rule creation with pattern matching
  • Language-specific security rules (Python, JavaScript, Go, Java, etc.)
  • CI/CD integration (GitHub Actions, GitLab CI, Jenkins)
  • False positive tuning and rule optimization
  • Organizational policy enforcement

2. SonarQube Setup

  • Quality gate configuration
  • Security hotspot analysis
  • Code coverage and technical debt tracking
  • Custom quality profiles for languages
  • Enterprise integration with LDAP/SAML

3. CodeQL Analysis

  • GitHub Advanced Security integration
  • Custom query development
  • Vulnerability variant analysis
  • Security research workflows
  • SARIF result processing

Quick Start

Initial Assessment

  1. Identify primary programming languages in your codebase
  2. Determine compliance requirements (PCI-DSS, SOC 2, etc.)
  3. Choose SAST tool based on language support and integration needs
  4. Review baseline scan to understand current security posture

Basic Setup

bash
# Semgrep quick start
pip install semgrep
semgrep --config=auto --error

# SonarQube with Docker
docker run -d --name sonarqube -p 9000:9000 sonarqube:latest

# CodeQL CLI setup
gh extension install github/gh-codeql
codeql database create mydb --language=python

Reference Documentation

Templates & Assets

Integration Patterns

CI/CD Pipeline Integration

yaml
# GitHub Actions example
- name: Run Semgrep
  uses: returntocorp/semgrep-action@v1
  with:
    config: >-
      p/security-audit
      p/owasp-top-ten

Pre-commit Hook

bash
# .pre-commit-config.yaml
- repo: https://github.com/returntocorp/semgrep
  rev: v1.45.0
  hooks:
    - id: semgrep
      args: ['--config=auto', '--error']

Best Practices

  1. Start with Baseline

    • Run initial scan to establish security baseline
    • Prioritize critical and high severity findings
    • Create remediation roadmap
  2. Incremental Adoption

    • Begin with security-focused rules
    • Gradually add code quality rules
    • Implement blocking only for critical issues
  3. False Positive Management

    • Document legitimate suppressions
    • Create allow lists for known safe patterns
    • Regularly review suppressed findings
  4. Performance Optimization

    • Exclude test files and generated code
    • Use incremental scanning for large codebases
    • Cache scan results in CI/CD
  5. Team Enablement

    • Provide security training for developers
    • Create internal documentation for common patterns
    • Establish security champions program

Common Use Cases

New Project Setup

bash
./scripts/run-sast.sh --setup --language python --tools semgrep,sonarqube

Custom Rule Development

yaml
# See references/semgrep-rules.md for detailed examples
rules:
  - id: hardcoded-jwt-secret
    pattern: jwt.encode($DATA, "...", ...)
    message: JWT secret should not be hardcoded
    severity: ERROR

Compliance Scanning

bash
# PCI-DSS focused scan
semgrep --config p/pci-dss --json -o pci-scan-results.json

Troubleshooting

High False Positive Rate

  • Review and tune rule sensitivity
  • Add path filters to exclude test files
  • Use nostmt metadata for noisy patterns
  • Create organization-specific rule exceptions

Performance Issues

  • Enable incremental scanning
  • Parallelize scans across modules
  • Optimize rule patterns for efficiency
  • Cache dependencies and scan results

Integration Failures

  • Verify API tokens and credentials
  • Check network connectivity and proxy settings
  • Review SARIF output format compatibility
  • Validate CI/CD runner permissions

Related Skills

Tool Comparison

ToolBest ForLanguage SupportCostIntegration
SemgrepCustom rules, fast scans30+ languagesFree/EnterpriseExcellent
SonarQubeCode quality + security25+ languagesFree/CommercialGood
CodeQLDeep analysis, research10+ languagesFree (OSS)GitHub native

Next Steps

  1. Complete initial SAST tool setup
  2. Run baseline security scan
  3. Create custom rules for organization-specific patterns
  4. Integrate into CI/CD pipeline
  5. Establish security gate policies
  6. Train development team on findings and remediation

Frequently asked questions

What does the Sast Configuration AI skill do?

Configure Static Application Security Testing (SAST) tools for automated vulnerability detection in application code. Use when setting up security scanning, implementing DevSecOps practices, or automating code vulnerability detection.

Why use Sast Configuration on TypingMind?

Because you install it once and use it with any model. Sast Configuration 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 Sast Configuration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ormus-Solutions/LibreUIUX-Claude-Code/tree/main/plugins/security-scanning/skills/sast-configuration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sast Configuration?

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 Sast Configuration?

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

Is the Sast Configuration AI skill free?

Yes. It is published on GitHub by Ormus-Solutions 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇