Atlas / Skills / wshobson / Sast Configuration

Sast ConfigurationSAFE

skills/wshobson/sast-configuration

Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
40,124
01

Overview

Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi

Read from source at commit adb71e0b2512OBSERVED · 2026-10-01
02

Install

Commands as the repository documents them. They are shown, not run.

pip install semgrep
03

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: sast-configuration
description: 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.
---

# 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:10.8-community

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

## 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

- [STRIDE Analysis Patterns](../stride-analysis-patterns/SKILL.md)
- [Threat Mitigation Mapping](../threat-mitigation-mapping/SKILL.md)

## Tool Comparison

| Tool      | Best For                 | Language Support | Cost            | Integration   |
| --------- | ------------------------ | ---------------- | --------------- | ------------- |
| Semgrep   | Custom rules, fast scans | 30+ languages    | Free/Enterprise | Excellent     |
| SonarQube | Code quality + security  | 25+ languages    | Free/Commercial | Good          |
| CodeQL    | Deep analysis, research  | 10+ languages    | Free (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
04

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (1)

LOWInventory / provenance · inv.symlink · CWE-1104
CLAUDE.md
CLAUDE.md
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-10-01 · audit v0.4.1 · source sha adb71e0b2512full audit observations/trust-audit/skill/wshobson__sast-configuration.json · Report an issue / request a re-scan
05

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-01adb71e0b2512SAFEB89first audit
06

Questions

What does the Sast Configuration skill do?

Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi

Is Sast Configuration safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Sast Configuration access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (adb71e0b2512), read on 2026-10-01. The repository is watched, and a new audit runs when it changes — this is the first audit.

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