ci-cd-pipeline-builder

SKILL.md

CI/CD Pipeline Builder

Tier: POWERFUL

Category: Engineering

Domain: DevOps / Automation

Overview

Use this skill to generate pragmatic CI/CD pipelines from detected project stack signals, not guesswork. It focuses on fast baseline generation, repeatable checks, and environment-aware deployment stages.

Core Capabilities

Detect language/runtime/tooling from repository files

Recommend CI stages (lint, test, build, deploy)

Generate GitHub Actions or GitLab CI starter pipelines

Include caching and matrix strategy based on detected stack

Emit machine-readable detection output for automation

Keep pipeline logic aligned with project lockfiles and build commands

When to Use

Bootstrapping CI for a new repository

Replacing brittle copied pipeline files

Migrating between GitHub Actions and GitLab CI

Auditing whether pipeline steps match actual stack

Creating a reproducible baseline before custom hardening

Key Workflows

1. Detect Stack

python3 scripts/stack_detector.py --repo . --format text
python3 scripts/stack_detector.py --repo . --format json > detected-stack.json

Supports input via stdin or --input file for offline analysis payloads.

2. Generate Pipeline From Detection

python3 scripts/pipeline_generator.py \
  --input detected-stack.json \
  --platform github \
  --output .github/workflows/ci.yml \
  --format text

Or end-to-end from repo directly:

python3 scripts/pipeline_generator.py --repo . --platform gitlab --output .gitlab-ci.yml

3. Validate Before Merge

Confirm commands exist in project (test, lint, build).

Run generated pipeline locally where possible.

Ensure required secrets/env vars are documented.

Keep deploy jobs gated by protected branches/environments.

4. Add Deployment Stages Safely

Start with CI-only (lint/test/build).

Add staging deploy with explicit environment context.

Add production deploy with manual gate/approval.

Keep rollout/rollback commands explicit and auditable.

Script Interfaces

python3 scripts/stack_detector.py --help

Detects stack signals from repository files

Reads optional JSON input from stdin/--input

python3 scripts/pipeline_generator.py --help

Generates GitHub/GitLab YAML from detection payload

Writes to stdout or --output

Common Pitfalls

Copying a Node pipeline into Python/Go repos

Enabling deploy jobs before stable tests

Forgetting dependency cache keys

Running expensive matrix builds for every trivial branch

Missing branch protections around prod deploy jobs

Hardcoding secrets in YAML instead of CI secret stores

Best Practices

Detect stack first, then generate pipeline.

Keep generated baseline under version control.

Add one optimization at a time (cache, matrix, split jobs).

Require green CI before deployment jobs.

Use protected environments for production credentials.

Regenerate pipeline when stack changes significantly.

References

references/github-actions-templates.md

references/gitlab-ci-templates.md

references/deployment-gates.md

README.md

Detection Heuristics

The stack detector prioritizes deterministic file signals over heuristics:

Lockfiles determine package manager preference

Language manifests determine runtime families

Script commands (if present) drive lint/test/build commands

Missing scripts trigger conservative placeholder commands

Generation Strategy

Start with a minimal, reliable pipeline:

Checkout and setup runtime

Install dependencies with cache strategy

Run lint, test, build in separate steps

Publish artifacts only after passing checks

Then layer advanced behavior (matrix builds, security scans, deploy gates).

Platform Decision Notes

GitHub Actions for tight GitHub ecosystem integration

GitLab CI for integrated SCM + CI in self-hosted environments

Keep one canonical pipeline source per repo to reduce drift

Validation Checklist

Generated YAML parses successfully.

All referenced commands exist in the repo.

Cache strategy matches package manager.

Required secrets are documented, not embedded.

Branch/protected-environment rules match org policy.

Scaling Guidance

Split long jobs by stage when runtime exceeds 10 minutes.

Introduce test matrix only when compatibility truly requires it.

Separate deploy jobs from CI jobs to keep feedback fast.

Track pipeline duration and flakiness as first-class metrics.