CI CD Pipeline Builder Skill for Claude
CI/CD Pipeline Builder
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/mar-antaya/my-claude-skills --skill ci-cd-pipeline-builderThe instruction itself
18 sections, as written by the authorCI/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.
Other skills for the same job
different authors, same section of the catalogue
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.
How to use it
Take mar-antaya/ci-cd-pipeline-builder 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.