CI/CD Pipeline Builder
SkillAI & modelsOnce added, your AI can detect what your project is built with and generate ready-to-use CI/CD pipeline files for GitHub Actions or GitLab CI, so you get automated checks and deployment stages without writing them by hand. It is useful for setting up CI on a new project, refactoring an existing pipeline, or standardizing deployment workflows across multiple repositories.
Available today. Use it from your connected AI after setup.
No other account needed.
Point your AI at a project and ask it to set up CI — it will detect the stack and generate the pipeline files. You can also ask it to refactor an existing pipeline or apply the same deployment workflow across several repositories.
Then ask your AI: use the CI/CD Pipeline Builder skill
What your AI can do with it
- Detect your project's stack from its files
- Generate ready-to-use pipeline files for GitHub Actions or GitLab CI
- Create a fast baseline pipeline when starting a new project
- Add repeatable checks that run consistently
- Set up deployment stages tailored to each environment
- Standardize deployment workflows across multiple repositories
What this skill tells your AI
The instructions your AI receives, as published by alirezarezvani/claude-skills in .gemini/skills/ci-cd-pipeline-builder/SKILL.md and read by ahel’s review.
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
References
- references/pipeline-design-notes.md — common pitfalls, best practices, detection heuristics, generation strategy, platform decision notes, pre-merge validation checklist, and scaling guidance
- references/github-actions-templates.md
- references/gitlab-ci-templates.md
- references/deployment-gates.md
- README.md
Signals
- GitHub stars
- 26k
- Forks
- 4k
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
ci-cd-pipeline-builder- Source
- github.com/alirezarezvani/claude-skills