> Design and generate CI/CD pipelines from project stack signals across GitHub Actions, GitLab CI, CircleCI, and Buildkite. Use when bootstrapping CI, migrating pipelines, adding deployment gates, or optimizing build times.
npx skills add https://github.com/borghei/Claude-Skills --skill ci-cd-pipeline-builder
Generate production-grade CI/CD pipelines from detected project stack signals. Analyzes lockfiles, manifests, and scripts to produce optimized pipelines with proper caching, matrix strategies, security scanning, and deployment gates. Supports GitHub Actions, GitLab CI, CircleCI, and Buildkite with deployment strategies including blue-green, canary, and rolling updates.
Keywords: CI/CD, GitHub Actions, GitLab CI, pipeline, deployment, caching, matrix builds, blue-green deployment, canary deployment, security scanning, SAST, container builds, environment gates
Before generating the pipeline, confirm these inputs. If any is unknown or vague, ASK — do not assume:
pipeline_generator.py emits via --platform)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command |
|------|---------|---------|
| pipeline_generator.py | Generate pipeline YAML from project stack detection | python scripts/pipeline_generator.py . --platform github --deploy |
| cache_optimizer.py | Analyze pipeline configs and suggest caching improvements | python scripts/cache_optimizer.py --dir . --severity high |
| pipeline_linter.py | Lint pipeline YAML for common issues | python scripts/pipeline_linter.py --dir . --severity warning |
Load the reference that matches the task — keep this file lean and pull detail on demand:
This skill covers:
This skill does NOT cover:
engineering/saas-scaffolder)engineering/skill-security-auditor)engineering/observability-designer)engineering/migration-architect)| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| engineering/dependency-auditor | Feeds vulnerability scan results into pipeline security gates | Auditor findings trigger pipeline failure or warning annotations |
| engineering/release-manager | Coordinates versioning and changelog with deploy stages | Release tags drive conditional deployment job execution |
| engineering/observability-designer | Post-deploy health checks and alerting complement pipeline gates | Pipeline triggers smoke tests; observability confirms deployment health |
| engineering/env-secrets-manager | Manages secrets referenced by pipeline environment variables | Secret rotation policies feed into pipeline secret store configuration |
| engineering/migration-architect | Database migrations run as a pre-deploy step in the pipeline | Migration status gates the application deployment job |
| engineering/runbook-generator | Generates rollback runbooks aligned with deployment strategy | Pipeline failure triggers link to the relevant rollback runbook |
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.
Take borghei/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.