Perform static application security testing with tools like Semgrep, CodeQL, and SonarQube. Identify security vulnerabilities in source code before deployment. Use when implementing secure SDLC, code review automation, or security gates in CI/CD pipelines.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill sast-scanning
Identify security vulnerabilities in source code through static analysis.
Use this skill when:
| Tool | License | Languages | Best For |
|------|---------|-----------|----------|
| Semgrep | OSS/Commercial | 30+ | Custom rules, speed |
| CodeQL | Free (GitHub) | 10+ | Deep analysis |
| SonarQube | OSS/Commercial | 25+ | Quality + Security |
| Bandit | OSS | Python | Python projects |
| Brakeman | OSS | Ruby | Rails apps |
# Install via pip
pip install semgrep
# Or via Homebrew
brew install semgrep
# Run with default rules
semgrep --config auto .
# Run specific rulesets
semgrep --config p/security-audit .
semgrep --config p/owasp-top-ten .
semgrep --config p/ci .
# Scan specific languages
semgrep --config p/python .
semgrep --config p/javascript .
# Output formats
semgrep --config auto --json -o results.json .
semgrep --config auto --sarif -o results.sarif .
# .semgrep/custom-rules.yaml
rules:
- id: hardcoded-password
patterns:
- pattern-either:
- pattern: password = "..."
- pattern: PASSWORD = "..."
- pattern: passwd = "..."
message: Hardcoded password detected
severity: ERROR
languages: [python, javascript, java]
metadata:
cwe: "CWE-798"
owasp: "A3:2017"
- id: sql-injection
patterns:
- pattern: |
$QUERY = "..." + $USER_INPUT + "..."
$DB.execute($QUERY)
message: Potential SQL injection
severity: ERROR
languages: [python]
metadata:
cwe: "CWE-89"
- id: insecure-random
pattern: random.random()
message: Use secrets module for security-sensitive randomness
severity: WARNING
languages: [python]
fix: secrets.token_hex()
# .github/workflows/semgrep.yml
name: Semgrep
on:
push:
branches: [main]
pull_request:
jobs:
semgrep:
runs-on: ubuntu-latest
container:
image: returntocorp/semgrep
steps:
- uses: actions/checkout@v4
- name: Run Semgrep
run: semgrep ci
env:
SEMGREP_APP_TOKEN: ${{ secrets.SEMGREP_APP_TOKEN }}
# .github/workflows/codeql.yml
name: CodeQL Analysis
on:
push:
branches: [main]
pull_request:
branches: [main]
schedule:
- cron: '0 0 * * 0'
jobs:
analyze:
runs-on: ubuntu-latest
permissions:
security-events: write
actions: read
contents: read
strategy:
matrix:
language: ['javascript', 'python']
steps:
- uses: actions/checkout@v4
- name: Initialize CodeQL
uses: github/codeql-action/init@v3
with:
languages: ${{ matrix.language }}
queries: +security-and-quality
- name: Autobuild
uses: github/codeql-action/autobuild@v3
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@v3
with:
category: "/language:${{ matrix.language }}"
// queries/sql-injection.ql
/**
* @name SQL Injection
* @description User input in SQL query
* @kind path-problem
* @problem.severity error
* @security-severity 9.0
* @precision high
* @id py/sql-injection
* @tags security
*/
import python
import semmle.python.dataflow.new.DataFlow
import semmle.python.dataflow.new.TaintTracking
import semmle.python.security.dataflow.SqlInjectionQuery
from SqlInjectionConfiguration config, DataFlow::PathNode source, DataFlow::PathNode sink
where config.hasFlowPath(source, sink)
select sink.getNode(), source, sink, "SQL injection from $@.", source.getNode(), "user input"
# docker-compose.yml
version: '3.8'
services:
sonarqube:
image: sonarqube:lts-community
ports:
- "9000:9000"
environment:
- SONAR_JDBC_URL=jdbc:postgresql://db:5432/sonar
- SONAR_JDBC_USERNAME=sonar
- SONAR_JDBC_PASSWORD=sonar
volumes:
- sonarqube_data:/opt/sonarqube/data
- sonarqube_logs:/opt/sonarqube/logs
depends_on:
- db
db:
image: postgres:15
environment:
- POSTGRES_USER=sonar
- POSTGRES_PASSWORD=sonar
- POSTGRES_DB=sonar
volumes:
- postgresql_data:/var/lib/postgresql/data
volumes:
sonarqube_data:
sonarqube_logs:
postgresql_data:
# sonar-project.properties
sonar.projectKey=myproject
sonar.projectName=My Project
sonar.projectVersion=1.0
sonar.sources=src
sonar.tests=tests
sonar.exclusions=**/node_modules/**,**/vendor/**
sonar.language=py
sonar.python.coverage.reportPaths=coverage.xml
sonar.qualitygate.wait=true
# GitHub Actions
- name: SonarQube Scan
uses: sonarsource/sonarqube-scan-action@master
env:
SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
SONAR_HOST_URL: ${{ secrets.SONAR_HOST_URL }}
- name: Quality Gate
uses: sonarsource/sonarqube-quality-gate-action@master
timeout-minutes: 5
env:
SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
# Install
pip install bandit
# Run scan
bandit -r src/ -f json -o bandit-report.json
# With configuration
bandit -r src/ -c bandit.yaml
# bandit.yaml
skips: ['B101', 'B601']
exclude_dirs: ['tests', 'venv']
assert_used:
skips: ['*_test.py', '*_tests.py']
# Install
npm install eslint eslint-plugin-security --save-dev
// .eslintrc.js
module.exports = {
plugins: ['security'],
extends: ['plugin:security/recommended'],
rules: {
'security/detect-object-injection': 'error',
'security/detect-non-literal-regexp': 'warn',
'security/detect-unsafe-regex': 'error',
'security/detect-buffer-noassert': 'error',
'security/detect-eval-with-expression': 'error',
'security/detect-no-csrf-before-method-override': 'error',
'security/detect-possible-timing-attacks': 'warn'
}
};
# Install
gem install brakeman
# Run scan
brakeman -o brakeman-report.json -f json
# CI configuration
brakeman --no-exit-on-warn --no-exit-on-error -o report.html
{
"name": "Security Gate",
"conditions": [
{
"metric": "new_security_rating",
"op": "GT",
"error": "1"
},
{
"metric": "new_vulnerabilities",
"op": "GT",
"error": "0"
},
{
"metric": "new_security_hotspots_reviewed",
"op": "LT",
"error": "100"
}
]
}
#!/bin/bash
# security-gate.sh
CRITICAL=$(cat results.json | jq '[.results[] | select(.severity == "critical")] | length')
HIGH=$(cat results.json | jq '[.results[] | select(.severity == "high")] | length')
echo "Critical: $CRITICAL, High: $HIGH"
if [ "$CRITICAL" -gt 0 ]; then
echo "FAILED: Critical vulnerabilities found"
exit 1
fi
if [ "$HIGH" -gt 5 ]; then
echo "FAILED: Too many high severity vulnerabilities"
exit 1
fi
echo "PASSED: Security gate"
exit 0
Problem: Alerts on safe code patterns
Solution: Tune rules, add suppressions, use baseline
Problem: SAST taking too long in CI
Solution: Incremental scanning, parallel execution, exclude test files
Problem: Vulnerabilities not detected
Solution: Add custom rules, combine multiple tools
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take bagelhole/sast-scanning from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, npm, brew, gem.
Without those the skill loads but fails at the first command.