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 aut...
mkdir -p ./skills/sast-configuration && curl -sfL https://raw.githubusercontent.com/mayurrathi/awesome-agent-skills/main/skills/sast-configuration/SKILL.md -o ./skills/sast-configuration/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# SAST Configuration
Static Application Security Testing (SAST) tool setup, configuration, and custom rule creation for comprehensive security scanning across multiple programming languages.
Use this skill when
- 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
Do not use this skill when
- You only need DAST or manual penetration testing guidance
- You cannot access source code or CI/CD pipelines
- You need organizational policy decisions rather than tooling setup
Instructions
1. Identify languages, repos, and compliance requirements.
2. Choose tools and define a baseline policy.
3. Integrate scans into CI/CD with gating thresholds.
4. Tune rules and suppressions based on false positives.
5. Track remediation and verify fixes.
Safety
- Avoid scanning sensitive repos with third-party services without approval.
- Prevent leaks of secrets in scan artifacts and logs.
Overview
This skill provides comprehensive guidance for setting up and configuring SAST tools including Semgrep, SonarQube, and CodeQL.
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
- Semgrep Rule Creation - Pattern-based security rule development
- SonarQube Configuration - Quality gates and profiles
- CodeQL Setup Guide - Query development and workflows
Templates & Assets
- semgrep-config.yml - Production-ready Semgrep configuration
- sonarqube-settings.xml - SonarQube quality profile template
- run-sast.sh - Automated SAST execution script
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 Enableme
🎯 Best For
- Security auditors
- DevSecOps teams
- Compliance officers
- QA engineers
- Developers writing unit tests
💡 Use Cases
- Auditing dependencies for known CVEs
- Scanning API endpoints for auth gaps
- Generating test cases for edge conditions
- Writing integration test suites
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Sast Configuration to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Can this replace a dedicated SAST tool?
AI-based security review is complementary to SAST tools. Use it as a first-pass filter, not a replacement.
Does this generate test mocks?
Many testing skills include mock generation. Check the install command and skill content for details.
Is Sast Configuration compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Sast Configuration?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Sast Configuration?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/sast-configuration/SKILL.md, ready to use.
⚠️ Common Mistakes to Avoid
Only scanning surface-level issues
Deep security review requires understanding your app architecture, not just regex patterns.
Not testing edge cases
AI tends to generate happy-path tests. Manually review for boundary conditions.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.