Ci Cd Pipeline Design

CI/CD Pipeline Design

Before you start

Gather the following from the user:

1. Which CI/CD platform? (GitHub Actions, GitLab CI, CircleCI, or platform-agnostic)
2. What language/runtime? (Node.js, Python, Go, Java, Rust, multi-language)
3. What needs to happen? (Lint, test, build, deploy — which of these?)
4. Where does it deploy? (Vercel, AWS, GCP, Kubernetes, static hosting)
5. What is the branching strategy? (Trunk-based, GitFlow, feature branches)
6. What is slow today? (If optimizing an existing pipeline, what takes the longest?)

If the user says "set up CI/CD," push back: "For which platform, language, and deployment target? I also need your branching strategy to design the trigger rules."

Procedure

Step 1: Define trigger rules

Map git events to pipeline runs:

# GitHub Actions example
on:
  pull_request:
    branches: [main]          # Run tests on PRs to main
  push:
    branches: [main]          # Deploy on merge to main
    tags: ["v*"]              # Deploy releases on version tags
  workflow_dispatch:           # Manual trigger for ad-hoc runs

Rules:

• PRs trigger lint + test + build (no deploy)
• Merge to main triggers lint + test + build + deploy to staging
• Tags trigger deploy to production
• Never auto-deploy to production on push to main

Step 2: Design the stage graph

Organize jobs into stages with dependency edges:

[Lint] ──┐
         ├──> [Build] ──> [Deploy Staging] ──> [Smoke Test] ──> [Deploy Prod]
[Test] ──┘

For each stage, document:

| Stage | Trigger | Depends on | Timeout | Runs on | |-------|---------|------------|---------|---------| | Lint | PR + push | None | 5 min | ubuntu-latest | | Test | PR + push | None | 15 min | ubuntu-latest | | Build | PR + push | Lint + Test pass | 10 min | ubuntu-latest | | Deploy staging | Push to main | Build | 10 min | ubuntu-latest | | Smoke test | After staging deploy | Deploy staging | 5 min | ubuntu-latest | | Deploy prod | Tag v* or manual | Smoke test pass | 10 min | ubuntu-latest |

Run Lint and Test in parallel. They have no dependency on each other.

Step 3: Configure caching

Caching is the single highest-impact optimization. For each ecosystem, cache the dependency directory with a key based on the lockfile hash:

• Node.js: Cache node_modules, key on hashFiles('pnpm-lock.yaml')
• Python: Cache ~/.cache/pip, key on hashFiles('requirements.txt')
• Go: Cache ~/go/pkg/mod + ~/.cache/go-build, key on hashFiles('go.sum')
• Docker: Use cache-from: type=gha and cache-to: type=gha,mode=max

Always set restore-keys for partial cache hits. Monitor cache hit rates — below 80% means the key strategy needs adjustment.

Step 4: Implement test parallelization

Split tests across parallel runners to reduce wall-clock time:

GitHub Actions matrix:

test:
  strategy:
    matrix:
      shard: [1, 2, 3, 4]
  steps:
    - run: npx jest --shard=${{ matrix.shard }}/4

CircleCI parallelism:

test:
  parallelism: 4
  steps:
    - run: |
        TESTS=$(circleci tests glob "**/*.test.ts" | circleci tests split --split-by=timings)
        npx jest $TESTS

Split by timing data when available. Fall back to file count splitting. Rebalance shards when test distribution becomes uneven.

Step 5: Configure secrets and environment variables

Use the platform's secret store (${{ secrets.X }} in GitHub Actions, CI/CD variables in GitLab). Rules:

• Never echo secrets in logs — use add-mask or equivalent
• Scope secrets per environment (staging vs production)
• For OIDC-capable targets (AWS, GCP), use identity federation instead of static credentials
• Document secret rotation procedure in the pipeline README

Step 6: Add deploy gates

Between staging and production, require at least one gate:

• Smoke test: Automated health check hitting critical endpoints on staging
• Manual approval: Required for production deploys (GitHub: environment with reviewers; GitLab: when: manual)
• Canary verification: Deploy to a subset, verify metrics, then proceed

# GitHub Actions environment protection
deploy-prod:
  environment:
    name: production
    url: https://app.example.com
  needs: [smoke-test]

Step 7: Handle artifacts

Build once, deploy the same artifact to all environments. Upload build output as a pipeline artifact with a retention policy. Download in deploy jobs. The artifact deployed to production must be byte-identical to what was tested in staging — never rebuild per environment.

Quality checklist

Before delivering the pipeline configuration, verify:

• [ ] PRs run lint + test + build but never deploy
• [ ] Lint and test run in parallel where possible
• [ ] Caching is configured for dependencies with lockfile-based keys
• [ ] Secrets use the platform's secret store, never hardcoded
• [ ] Production deploy requires a gate (manual approval or automated canary)
• [ ] Build artifacts are created once and reused across environments
• [ ] Timeouts are set on every job to prevent hung pipelines
• [ ] The pipeline YAML is syntactically valid for the target platform

Common mistakes

• Deploying to production on push to main. Always gate production deploys behind manual approval or automated verification. A broken merge should not reach users automatically.
• No caching. Installing dependencies from scratch on every run wastes 2-5 minutes. Cache aggressively and monitor hit rates.
• Rebuilding per environment. Building separately for staging and production means you are deploying untested artifacts to production. Build once, deploy everywhere.
• Sequential lint and test. These have no dependency on each other. Run them in parallel to cut pipeline time.
• Secrets in pipeline YAML. Even "temporary" secrets in YAML files end up in git history. Use the platform's secret management from day one.
• No timeout on jobs. A hung test suite or a stuck deploy can block the pipeline for hours. Set explicit timeouts on every job.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: The-AI-Directory-Company
• Source: The-AI-Directory-Company/agents-and-skills
• License: MIT
• Homepage: https://ai-directory.company

Install and usage instructions live in the source repository linked above.