Constructs secure, efficient CI/CD pipelines with supply chain security (SLSA), monorepo optimization, caching strategies, and parallelization patterns for GitHub Actions, GitLab CI, and Argo Workflows. Use when setting up automated testing, building, or deployment workflows.
npx skills add https://github.com/ancoleman/ai-design-components --skill building-ci-pipelines
CI/CD pipelines automate testing, building, and deploying software. This skill provides patterns for constructing robust, secure, and efficient pipelines across GitHub Actions, GitLab CI, Argo Workflows, and Jenkins. Focus areas: supply chain security (SLSA), monorepo optimization, caching, and parallelization.
Invoke when:
GitHub-hosted → GitHub Actions (SLSA native, 10K+ actions, OIDC)
GitLab-hosted → GitLab CI (parent-child pipelines, built-in security)
Kubernetes → Argo Workflows (DAG-based, event-driven)
Legacy → Jenkins (migrate when possible)
| Feature | GitHub Actions | GitLab CI | Argo | Jenkins |
|---------|---------------|-----------|------|---------|
| Ease of Use | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
| SLSA | Native | Manual | Good | Manual |
| Monorepo | Good | Excellent | Manual | Plugins |
# GitHub Actions
name: CI
on: [push, pull_request]
jobs:
lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npm run lint
test:
needs: lint
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npm test
build:
needs: test
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npm run build
test:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
node-version: [18, 20, 22]
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: ${{ matrix.node-version }}
- run: npm test
9 jobs (3 OS × 3 versions) in parallel: 5 min vs 45 min sequential.
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0 # Required for affected detection
- uses: actions/setup-node@v4
with:
node-version: 20
- name: Build affected
run: npx turbo run build --filter='...[origin/main]'
env:
TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
TURBO_TEAM: ${{ vars.TURBO_TEAM }}
60-80% CI time reduction for monorepos.
name: SLSA Build
on:
push:
tags: ['v*']
permissions:
id-token: write
contents: read
packages: write
jobs:
build:
runs-on: ubuntu-latest
outputs:
digest: ${{ steps.build.outputs.digest }}
steps:
- uses: actions/checkout@v4
- name: Build container
id: build
uses: docker/build-push-action@v5
with:
push: true
tags: ghcr.io/${{ github.repository }}:${{ github.sha }}
provenance:
needs: build
permissions:
id-token: write
actions: read
packages: write
uses: slsa-framework/slsa-github-generator/.github/workflows/[email protected]
with:
image: ghcr.io/${{ github.repository }}
digest: ${{ needs.build.outputs.digest }}
registry-username: ${{ github.actor }}
secrets:
registry-password: ${{ secrets.GITHUB_TOKEN }}
Verification:
cosign verify-attestation --type slsaprovenance \
--certificate-identity-regexp "^https://github.com/slsa-framework" \
--certificate-oidc-issuer https://token.actions.githubusercontent.com \
ghcr.io/myorg/myapp@sha256:abcd...
deploy:
runs-on: ubuntu-latest
permissions:
id-token: write
contents: read
steps:
- uses: actions/checkout@v4
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::123456789012:role/GitHubActionsRole
aws-region: us-east-1
- name: Deploy
run: aws s3 sync ./dist s3://my-bucket
Benefits: No stored credentials, 1-hour lifetime, full audit trail.
security:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Gitleaks (secret detection)
uses: gitleaks/gitleaks-action@v2
- name: Snyk (vulnerability scan)
uses: snyk/actions/node@master
env:
SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }}
- name: SBOM generation
uses: anchore/sbom-action@v0
with:
format: spdx-json
output-file: sbom.spdx.json
- uses: actions/setup-node@v4
with:
node-version: 20
cache: 'npm' # Auto-caches ~/.npm
- run: npm ci
Supported: npm, yarn, pnpm, pip, poetry, cargo, go
- uses: actions/cache@v4
with:
path: |
~/.cargo/bin
~/.cargo/registry
target/
key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
restore-keys: |
${{ runner.os }}-cargo-
- name: Nx Cloud (build outputs)
run: npx nx affected -t build
env:
NX_CLOUD_ACCESS_TOKEN: ${{ secrets.NX_CLOUD_ACCESS_TOKEN }}
- name: Vite Cache
uses: actions/cache@v4
with:
path: '**/node_modules/.vite'
key: vite-${{ hashFiles('package-lock.json') }}
- name: TypeScript Cache
uses: actions/cache@v4
with:
path: '**/tsconfig.tsbuildinfo'
key: tsc-${{ hashFiles('tsconfig.json') }}
Result: 70-90% build time reduction.
jobs:
unit-tests:
steps:
- run: npm run test:unit
integration-tests:
steps:
- run: npm run test:integration
e2e-tests:
steps:
- run: npm run test:e2e
All three run simultaneously.
test:
strategy:
matrix:
shard: [1, 2, 3, 4]
steps:
- run: npm test -- --shard=${{ matrix.shard }}/4
20min test suite → 5min (4x speedup).
test:
strategy:
matrix:
python-version: ['3.10', '3.11', '3.12']
steps:
- uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- run: pipx install poetry
- run: poetry install
- run: poetry run ruff check .
- run: poetry run mypy .
- run: poetry run pytest --cov
test:
strategy:
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
rust: [stable, nightly]
steps:
- uses: dtolnay/rust-toolchain@master
with:
toolchain: ${{ matrix.rust }}
components: rustfmt, clippy
- uses: Swatinem/rust-cache@v2
- run: cargo fmt -- --check
- run: cargo clippy -- -D warnings
- run: cargo test
test:
steps:
- uses: actions/setup-go@v5
with:
go-version: '1.23'
cache: true
- run: go mod verify
- uses: golangci/golangci-lint-action@v4
- run: go test -v -race -coverprofile=coverage.txt ./...
test:
strategy:
matrix:
node-version: [18, 20, 22]
steps:
- uses: pnpm/action-setup@v3
with:
version: 8
- uses: actions/setup-node@v4
with:
node-version: ${{ matrix.node-version }}
cache: 'pnpm'
- run: pnpm install --frozen-lockfile
- run: pnpm run lint
- run: pnpm run type-check
- run: pnpm test
DO:
actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11permissions: { contents: read }DON'T:
pull_request_target without validationDO:
strategy.fail-fast: trueDON'T:
# Enable debug logging
env:
ACTIONS_STEP_DEBUG: true
ACTIONS_RUNNER_DEBUG: true
# SSH into runner
- uses: mxschmitt/action-tmate@v3
For detailed guides, see references:
Complete runnable workflows:
Token-free execution:
testing-strategies - Test execution strategies (unit, integration, E2E)
deploying-applications - Deployment automation and GitOps
auth-security - Secrets management and authentication
observability - Pipeline monitoring and alerting
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 ancoleman/building-ci-pipelines 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, npx.
Without those the skill loads but fails at the first command.