Sast Configuration

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

# 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

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

Pre-commit Hook

# .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

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

Custom Rule Development

# 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

# 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

• OWASP Top 10 Checklist
• Container Security
• Dependency Scanning

Tool Comparison

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

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache compliance check results to avoid re-running expensive AWS API calls. Retrieve prior audit findings to track remediation progress across sessions.

# Check for prior security context before starting
python3 execution/memory_manager.py auto --query "prior security audit results for Sast Configuration"

Storing Results

After completing work, store security decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Audit findings: 3 critical IAM misconfigurations found and remediated" \
  --type technical --project <project> \
  --tags sast-configuration security

Multi-Agent Collaboration

Share security findings with other agents so they avoid introducing vulnerabilities in their code changes.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Completed security audit — 3 critical findings fixed, compliance score 94%" \
  --project <project>

Signed Audit Trail

All security findings are cryptographically signed with the agent's Ed25519 identity, providing tamper-proof audit logs for compliance reporting.

Semantic Cache for Compliance

Cache compliance check results (semantic_cache.py) to avoid redundant AWS API calls. Cache hit at similarity >0.92 returns prior results instantly.

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