CI/CD pipeline design, optimization, DevSecOps security scanning, and troubleshooting. Use this skill whenever the user mentions CI/CD, GitHub Actions, GitLab CI, pipelines, workflows, builds, or DevSecOps. Triggers include creating new CI/CD workflows, debugging pipeline failures or flaky tests, implementing SAST/DAST/SCA security scanning, optimizing slow builds with caching or parallelization, setting up deployment workflows, securing pipelines with OIDC or secrets management, implementing matrix builds or test sharding, and troubleshooting Docker, permissions, or timeout issues.
npx skills add https://github.com/ahmedasmar/devops-claude-skills --skill ci-cd
Comprehensive guide for CI/CD pipeline design, optimization, security, and troubleshooting across GitHub Actions, GitLab CI, and other platforms.
Decision tree:
What are you building?
├── Node.js/Frontend → GitHub: templates/github-actions/node-ci.yml | GitLab: templates/gitlab-ci/node-ci.yml
├── Python → GitHub: templates/github-actions/python-ci.yml | GitLab: templates/gitlab-ci/python-ci.yml
├── Go → GitHub: templates/github-actions/go-ci.yml | GitLab: templates/gitlab-ci/go-ci.yml
├── Docker Image → GitHub: templates/github-actions/docker-build.yml | GitLab: templates/gitlab-ci/docker-build.yml
├── Other → Follow the pipeline design pattern below
Basic pipeline structure:
# 1. Fast feedback (lint, format) - <1 min
# 2. Unit tests - 1-5 min
# 3. Integration tests - 5-15 min
# 4. Build artifacts
# 5. E2E tests (optional, main branch only) - 15-30 min
# 6. Deploy (with approval gates)
Key principles (from references/best_practices.md):
actions/cache or GitLab cache (references/optimization.md for strategies)references/devsecops.md for SAST/DAST/SCA integrationQuick wins checklist:
needs dependenciesnpm ci instead of npm installAnalyze existing pipeline:
# Use the pipeline analyzer script
python3 scripts/pipeline_analyzer.py --platform github --workflow .github/workflows/ci.yml
Common optimizations (detailed in references/optimization.md):
needsSee optimization.md for detailed caching strategies, parallelization techniques, and performance tuning.
Essential security checklist:
Quick setup - OIDC authentication:
GitHub Actions → AWS:
permissions:
id-token: write
contents: read
steps:
- uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::123456789:role/GitHubActionsRole
aws-region: us-east-1
Secrets management:
See security.md for comprehensive security patterns, supply chain security, and secrets management.
Systematic approach:
Step 1: Check pipeline health
gh run list --limit 20 # Recent runs with status (success/failure rates)
gh run view <run-id> # Detailed run info and failure logs
gh workflow list # All configured workflows
Step 2: Identify the failure type
| Error Pattern | Common Cause | Quick Fix |
|---------------|--------------|-----------|
| "Module not found" | Missing dependency or cache issue | Clear cache, run npm ci |
| "Timeout" | Job taking too long | Add caching, increase timeout |
| "Permission denied" | Missing permissions | Add to permissions: block |
| "Cannot connect to Docker daemon" | Docker not available | Use correct runner or DinD |
| Intermittent failures | Flaky tests or race conditions | Add retries, fix timing issues |
Step 3: Enable debug logging
GitHub Actions:
# Add repository secrets:
# ACTIONS_RUNNER_DEBUG = true
# ACTIONS_STEP_DEBUG = true
GitLab CI:
variables:
CI_DEBUG_TRACE: "true"
Step 4: Reproduce locally
# GitHub Actions - use act
act -j build
# Or Docker
docker run -it ubuntu:latest bash
# Then manually run the failing steps
See troubleshooting.md for comprehensive issue diagnosis, platform-specific problems, and solutions.
Deployment pattern selection:
| Pattern | Use Case | Complexity | Risk |
|---------|----------|------------|------|
| Direct | Simple apps, low traffic | Low | Medium |
| Blue-Green | Zero downtime required | Medium | Low |
| Canary | Gradual rollout, monitoring | High | Very Low |
| Rolling | Kubernetes, containers | Medium | Low |
Basic deployment structure:
deploy:
needs: [build, test]
if: github.ref == 'refs/heads/main'
environment:
name: production
url: https://example.com
steps:
- name: Download artifacts
- name: Deploy
- name: Health check
- name: Rollback on failure
Multi-environment setup:
See best_practices.md for detailed deployment patterns and environment management.
Security scanning types:
| Scan Type | Purpose | When to Run | Speed | Tools |
|-----------|---------|-------------|-------|-------|
| Secret Scanning | Find exposed credentials | Every commit | Fast (<1 min) | TruffleHog, Gitleaks |
| SAST | Find code vulnerabilities | Every commit | Medium (5-15 min) | CodeQL, Semgrep, Bandit, Gosec |
| SCA | Find dependency vulnerabilities | Every commit | Fast (1-5 min) | npm audit, pip-audit, Snyk |
| Container Scanning | Find image vulnerabilities | After build | Medium (5-10 min) | Trivy, Grype |
| DAST | Find runtime vulnerabilities | Scheduled/main only | Slow (15-60 min) | OWASP ZAP |
Quick setup - Add security to existing pipeline:
GitHub Actions:
jobs:
# Add before build job
secret-scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: trufflesecurity/trufflehog@main
- uses: gitleaks/gitleaks-action@v2
sast:
runs-on: ubuntu-latest
permissions:
security-events: write
steps:
- uses: actions/checkout@v4
- uses: github/codeql-action/init@v3
with:
languages: javascript # or python, go
- uses: github/codeql-action/analyze@v3
build:
needs: [secret-scan, sast] # Add dependencies
GitLab CI:
stages:
- security # Add before other stages
- build
- test
# Secret scanning
secret-scan:
stage: security
image: trufflesecurity/trufflehog:latest
script:
- trufflehog filesystem . --json --fail
# SAST
sast:semgrep:
stage: security
image: returntocorp/semgrep
script:
- semgrep scan --config=auto .
# Use GitLab templates
include:
- template: Security/SAST.gitlab-ci.yml
- template: Security/Dependency-Scanning.gitlab-ci.yml
Comprehensive security pipeline templates:
templates/github-actions/security-scan.yml - Complete DevSecOps pipeline with all scanning stagestemplates/gitlab-ci/security-scan.yml - Complete DevSecOps pipeline with GitLab security templatesSecurity gate pattern:
Add a security gate job that evaluates all security scan results and fails the pipeline if critical issues are found:
security-gate:
needs: [secret-scan, sast, sca, container-scan]
script:
# Check for critical vulnerabilities
# Parse JSON reports and evaluate thresholds
# Fail if critical issues found
Language-specific security tools:
All language-specific templates now include security scanning stages. See:
templates/github-actions/node-ci.ymltemplates/github-actions/python-ci.ymltemplates/github-actions/go-ci.ymltemplates/gitlab-ci/node-ci.ymltemplates/gitlab-ci/python-ci.ymltemplates/gitlab-ci/go-ci.ymlSee devsecops.md for comprehensive DevSecOps guide covering all security scanning types, tool comparisons, and implementation patterns.
# List workflows
gh workflow list
# View recent runs
gh run list --limit 20
# View specific run
gh run view <run-id>
# Re-run failed jobs
gh run rerun <run-id> --failed
# Download logs
gh run view <run-id> --log > logs.txt
# Trigger workflow manually
gh workflow run ci.yml
# Check workflow status
gh run watch
# View pipelines
gl project-pipelines list
# Pipeline status
gl project-pipeline get <pipeline-id>
# Retry failed jobs
gl project-pipeline retry <pipeline-id>
# Cancel pipeline
gl project-pipeline cancel <pipeline-id>
# Download artifacts
gl project-job artifacts <job-id>
Reusable workflows:
# .github/workflows/reusable-test.yml
on:
workflow_call:
inputs:
node-version:
required: true
type: string
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/setup-node@v4
with:
node-version: ${{ inputs.node-version }}
Call from another workflow:
jobs:
test:
uses: ./.github/workflows/reusable-test.yml
with:
node-version: '20'
Templates with extends:
.test_template:
image: node:20
before_script:
- npm ci
unit-test:
extends: .test_template
script:
- npm run test:unit
integration-test:
extends: .test_template
script:
- npm run test:integration
DAG pipelines with needs:
build:
stage: build
test:unit:
stage: test
needs: [build]
test:integration:
stage: test
needs: [build]
deploy:
stage: deploy
needs: [test:unit, test:integration]
| Script | Purpose | Usage |
|--------|---------|-------|
| pipeline_analyzer.py | Find optimization opportunities (caching, parallelization, outdated actions) | python3 scripts/pipeline_analyzer.py --platform github --workflow <path> |
For pipeline health checks (success/failure rates, failure patterns), use gh CLI: gh run list --limit 20, gh run view <run-id>, gh workflow list.
references/best_practices.md — Pipeline design, testing, deployment patterns, artifact handlingreferences/security.md — Secrets management, OIDC, supply chain security, secure pipeline patternsreferences/devsecops.md — SAST/DAST/SCA tooling (CodeQL, Semgrep, Trivy, Snyk), security gatesreferences/optimization.md — Caching strategies, parallelization, test splitting, build optimizationreferences/troubleshooting.md — Common issues, Docker problems, authentication, platform debuggingStarter templates in assets/templates/ for both GitHub Actions and GitLab CI:
| Language/Type | GitHub Actions | GitLab CI |
|---------------|---------------|-----------|
| Node.js | github-actions/node-ci.yml | gitlab-ci/node-ci.yml |
| Python | github-actions/python-ci.yml | gitlab-ci/python-ci.yml |
| Go | github-actions/go-ci.yml | gitlab-ci/go-ci.yml |
| Docker | github-actions/docker-build.yml | gitlab-ci/docker-build.yml |
| Security | github-actions/security-scan.yml | gitlab-ci/security-scan.yml |
All templates include security scanning, caching, and multi-environment deployment.
GitHub Actions:
- uses: actions/cache@v4
with:
path: ~/.npm
key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}
restore-keys: |
${{ runner.os }}-node-
- run: npm ci
GitLab CI:
cache:
key:
files:
- package-lock.json
paths:
- node_modules/
GitHub Actions:
strategy:
matrix:
os: [ubuntu-latest, macos-latest]
node: [18, 20, 22]
fail-fast: false
GitLab CI:
test:
parallel:
matrix:
- NODE_VERSION: ['18', '20', '22']
GitHub Actions:
- name: Deploy
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
GitLab CI:
deploy:
rules:
- if: '$CI_COMMIT_BRANCH == "main"'
when: manual
assets/templates/scripts/pipeline_analyzer.pyreferences/troubleshooting.mdreferences/security.md and references/devsecops.md checklistsreferences/optimization.mdAssess 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 ahmedasmar/ci-cd 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 npm, docker.
Without those the skill loads but fails at the first command.