Agent skill

Building CI Pipelines

by ancoleman in ancoleman/ai-design-components

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…

MITAuto-check passedDevOps & Cloud

Install Building CI Pipelines

skills CLI
$ npx skills add ancoleman/ai-design-components --skill building-ci-pipelines -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ancoleman/ai-design-components building-ci-pipelines --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/building-ci-pipelines .claude/skills/building-ci-pipelines && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
building-ci-pipelines
GitHub stars
525
Token cost
~2.7k tokens
SKILL.md length
477 words
Files
12 (incl. scripts, references)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Setting up automated testing
  • SKILL.md covers Purpose, When to Use This Skill, Platform Selection and Quick Start Patterns, plus 6 more sections
  • Runs Python scripts from its folder; reaches github.com and token.actions.githubusercontent.com; needs TURBO_TOKEN and SNYK_TOKEN
  • Deployment workflows

What it does

Building CI Pipelines is an agent skill from ancoleman/ai-design-components. 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.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts and reference files (for example `examples/github-actions-basic/.github/workflows/ci.yml`, `examples/github-actions-basic/README.md` and `examples/github-actions-monorepo/.github/workflows/ci.yml`).

It sits in DevOps & Cloud, covering CI/CD, Supply chain security and Monorepo tooling. It works with GitLab, GitHub Actions and Jenkins. The repository describes itself as: Comprehensive UI/UX and Backend component design skills for AI-assisted development with Claude. The licence is MIT.

When your agent uses it

  • Setting up automated testing
  • Deployment workflows

Example prompts

  • “Use the building-ci-pipelines skill to construct secure, efficient CI/CD pipelines with supply chain security (SLSA), monorepo optimization, caching…”
  • “/building-ci-pipelines”

Requirements

  • Python 3
  • Node.js
  • Docker
  • A credential in TURBO_TOKEN
  • A credential in GITHUB_TOKEN

What it can do on your machine

Read from SKILL.md and the folder at commit 76551b7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • token.actions.githubusercontent.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • TURBO_TOKEN
    • SNYK_TOKEN
    • NX_CLOUD_ACCESS_TOKEN
    • GITHUB_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Building CI Pipelines loads about 2.7k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 477 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~17k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 477 words, ~2,730 tokens.

Download SKILL.mdSave it as .claude/skills/building-ci-pipelines/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
building-ci-pipelines
description
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.

Building CI Pipelines

Purpose

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.

When to Use This Skill

Invoke when:

  • Setting up continuous integration for new projects
  • Implementing automated testing workflows
  • Building container images with security provenance
  • Optimizing slow CI pipelines (especially monorepos)
  • Implementing SLSA supply chain security
  • Configuring multi-platform builds
  • Setting up GitOps automation
  • Migrating from legacy CI systems

Platform Selection

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)

Platform Comparison
FeatureGitHub ActionsGitLab CIArgoJenkins
Ease of Use⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
SLSANativeManualGoodManual
MonorepoGoodExcellentManualPlugins

Quick Start Patterns

Pattern 1: Basic CI (Lint → Test → Build)
yaml
# 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
Pattern 2: Matrix Strategy (Multi-Platform)
yaml
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.

Pattern 3: Monorepo Affected (Turborepo)
yaml
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.

Pattern 4: SLSA Level 3 Provenance
yaml
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/generator_container_slsa3.yml@v1.10.0
    with:
      image: ghcr.io/${{ github.repository }}
      digest: ${{ needs.build.outputs.digest }}
      registry-username: ${{ github.actor }}
    secrets:
      registry-password: ${{ secrets.GITHUB_TOKEN }}

Verification:

bash
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...
Pattern 5: OIDC Federation (No Credentials)
yaml
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.

Pattern 6: Security Scanning
yaml
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

Caching

Automatic Dependency Caching
yaml
- 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

Manual Cache Control
yaml
- uses: actions/cache@v4
  with:
    path: |
      ~/.cargo/bin
      ~/.cargo/registry
      target/
    key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
    restore-keys: |
      ${{ runner.os }}-cargo-
Multi-Layer Caching (Nx)
yaml
- 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.

Parallelization

Job-Level Parallelization
yaml
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 Sharding
yaml
test:
  strategy:
    matrix:
      shard: [1, 2, 3, 4]
  steps:
    - run: npm test -- --shard=${{ matrix.shard }}/4

20min test suite → 5min (4x speedup).

Language Examples

Python
yaml
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
Rust
yaml
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
Go
yaml
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 ./...
TypeScript
yaml
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

Best Practices

Security

DO:

  • Use OIDC instead of long-lived credentials
  • Pin actions to commit SHA: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11
  • Restrict permissions: permissions: { contents: read }
  • Scan secrets (Gitleaks) on every commit
  • Generate SLSA provenance for releases

DON'T:

  • Expose secrets in logs
  • Use pull_request_target without validation
  • Trust unverified third-party actions
Show full SKILL.md (183 more words)Show less
Performance

DO:

  • Use affected detection for monorepos
  • Cache dependencies and build outputs
  • Parallelize independent jobs
  • Fail fast: strategy.fail-fast: true
  • Use remote caching (Turborepo/Nx Cloud)

DON'T:

  • Rebuild everything on every commit
  • Run long tests in PR checks
  • Use generic cache keys
Debugging
yaml
# Enable debug logging
env:
  ACTIONS_STEP_DEBUG: true
  ACTIONS_RUNNER_DEBUG: true

# SSH into runner
- uses: mxschmitt/action-tmate@v3

Advanced Patterns

For detailed guides, see references:

  • github-actions-patterns.md - Reusable workflows, composite actions, matrix strategies, OIDC setup
  • gitlab-ci-patterns.md - Parent-child pipelines, dynamic generation, runner configuration
  • argo-workflows-guide.md - DAG templates, artifact passing, event-driven triggers
  • slsa-security-framework.md - SLSA Levels 1-4, provenance generation, cosign verification
  • monorepo-ci-strategies.md - Turborepo/Nx/Bazel affected detection algorithms
  • caching-strategies.md - Multi-layer caching, Docker optimization, cache invalidation
  • parallelization-patterns.md - Test sharding, job dependencies, DAG design
  • secrets-management.md - OIDC for AWS/GCP/Azure, Vault integration, rotation

Examples

Complete runnable workflows:

  • examples/github-actions-basic/ - Starter template (lint/test/build)
  • examples/github-actions-monorepo/ - Turborepo with remote caching
  • examples/github-actions-slsa/ - SLSA Level 3 provenance
  • examples/gitlab-ci-monorepo/ - Parent-child dynamic pipeline
  • examples/argo-workflows-dag/ - Diamond DAG parallelization
  • examples/multi-language-matrix/ - Cross-platform testing

Utility Scripts

Token-free execution:

  • scripts/validate_workflow.py - Validate YAML syntax and best practices
  • scripts/generate_github_workflow.py - Generate workflow from template
  • scripts/analyze_ci_performance.py - CI metrics analysis
  • scripts/setup_oidc_aws.py - Automate AWS OIDC setup

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

© ancoleman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references) in skills/building-ci-pipelines of ancoleman/ai-design-components.

  • SKILL.md
  • examples/github-actions-basic/.github/workflows/ci.yml
  • examples/github-actions-basic/README.md
  • examples/github-actions-monorepo/.github/workflows/ci.yml
  • examples/github-actions-monorepo/README.md
  • examples/github-actions-monorepo/turbo.json
  • outputs.yaml
  • references/caching-strategies.md
  • references/github-actions-patterns.md
  • references/monorepo-ci-strategies.md
  • references/slsa-security-framework.md
  • scripts/validate_workflow.py

Open the folder on GitHubat commit 76551b7

Compare with similar skills

Building CI Pipelines next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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CI/CD Pipeline Principlesirahardianto/awesome-agv156—~2.7kAutomated safety check: NotesMIT
Playwright CIzebbern/claude-code-guide4.7k2 repos~675Automated safety check: PassMIT
CI CDRightNow-AI/openfang18k—~691Automated safety check: PassApache-2.0
Newman Cicd Integrationsickn33/agentic-awesome-skills47k1 repos~2.2kAutomated safety check: PassMIT

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Categories

Questions about Building CI Pipelines

What does Building CI Pipelines do?

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…. Building CI Pipelines is an agent skill from ancoleman/ai-design-components. 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.

When should I use Building CI Pipelines?

Building CI Pipelines fits situations like: setting up automated testing; deployment workflows.

How do I install Building CI Pipelines in Claude Code?

Run `npx skills add ancoleman/ai-design-components --skill building-ci-pipelines -a claude-code`. Or copy the skill folder (skills/building-ci-pipelines in ancoleman/ai-design-components) into .claude/skills/building-ci-pipelines in your project. Claude Code loads it when a task matches its description.

How do I install Building CI Pipelines in Codex?

Run `npx skills add ancoleman/ai-design-components --skill building-ci-pipelines -a codex`. Or copy the skill folder (skills/building-ci-pipelines in ancoleman/ai-design-components) into .agents/skills/building-ci-pipelines in your project. Codex loads it when a task matches its description.

Can I use Building CI Pipelines in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ancoleman/ai-design-components --skill building-ci-pipelines -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/building-ci-pipelines, .gemini/skills/building-ci-pipelines, .github/skills/building-ci-pipelines and .opencode/skills/building-ci-pipelines in your project.

What does Building CI Pipelines need to run?

Going by SKILL.md and its folder, Building CI Pipelines needs Python for the scripts in its folder and credentials named TURBO_TOKEN, SNYK_TOKEN, NX_CLOUD_ACCESS_TOKEN and GITHUB_TOKEN. Our summary lists: Python 3; Node.js; Docker; A credential in TURBO_TOKEN; A credential in GITHUB_TOKEN.

Does Building CI Pipelines access the network?

SKILL.md names 2 domains. In commands or code: github.com and token.actions.githubusercontent.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Building CI Pipelines safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Building CI Pipelines use?

Building CI Pipelines is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Building CI Pipelines use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 14k tokens, read only when the agent opens those files.

What are the alternatives to Building CI Pipelines?

Skills that share tags, products or a category with Building CI Pipelines: Tirith Policies (StackGuardian/tirith, 170 stars), CI/CD Pipeline Principles (irahardianto/awesome-agv, 156 stars), Playwright CI (zebbern/claude-code-guide, 4.7k stars) and CI CD (RightNow-AI/openfang, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Building CI Pipelines?

ancoleman (a GitHub user) maintains it in ancoleman/ai-design-components, which has 525 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on December 11, 2025.

Source: ancoleman/ai-design-components on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.