Ci generate

CI/CD Pipeline Generator

Generate production-ready CI/CD pipeline configurations with auto-detected project stack, current best practices, and comprehensive stages (lint, test, build, security scan, deploy).

Pipeline to Generate

Parse the arguments passed to this skill invocation (platform, --deploy, --monorepo) — see Phase 0 below.

Anti-Hallucination Guidelines

CRITICAL: Pipeline configurations must match ACTUAL project tooling:

1. Discover before generating - Never assume package managers, test runners, or build tools
2. Verify commands exist - Check package.json scripts, Makefile targets, pyproject.toml scripts before referencing them
3. Match real versions - Use actual language/runtime versions from project config files (.node-version, .python-version, pyproject.toml, etc.)
4. No phantom dependencies - Only include services (Redis, PostgreSQL, etc.) confirmed in project dependencies
5. Platform-specific syntax - Each CI platform has distinct YAML structure; never mix syntax between platforms

Workflow

Phase 0: Parse Arguments

Extract configuration from the arguments passed to this skill:

Arguments:
- <platform>: Target CI platform (default: auto-detect from existing config)
 - "github" or "gh": GitHub Actions
 - "gitlab" or "gl": GitLab CI
 - "circle" or "circleci": CircleCI
 - "jenkins": Jenkins (Jenkinsfile)
- "--deploy <target>": Deployment target (optional)
 - "vercel", "netlify", "aws", "gcp", "azure", "docker", "k8s", "fly", "railway"
- "--monorepo": Generate matrix/path-filtered workflows
- No args: Auto-detect platform from existing CI config files, default to GitHub Actions

If no platform specified, detect from existing files:
- `.github/workflows/*.yml` → GitHub Actions
- `.gitlab-ci.yml` → GitLab CI
- `.circleci/config.yml` → CircleCI
- `Jenkinsfile` → Jenkins
- No CI config found → Default to GitHub Actions

Phase 1: Project Stack Detection

Explore the codebase to discover the complete project technology stack.

Phase 2: Research Best Practices (only if needed)

references/platform-patterns.md already ships current, comprehensive patterns for Node/Python/matrix/Docker/deploy across all four platforms — check it first. Only reach for WebSearch when the detected stack/platform combo isn't covered there (e.g. an unusual language or a deploy target not in the reference):

Use WebSearch:
- query: "[detected platform] CI/CD best practices [detected language] [current year]"

Use WebSearch:
- query: "[detected platform] security scanning pipeline [detected language] [current year]"

Focus on:

• Caching strategies for the detected package manager
• Recommended runner images and versions
• Security scanning tools appropriate for the language
• Deployment best practices for the target platform
• Matrix testing strategies if multiple versions needed

Phase 3: Design Pipeline Architecture

Based on discovery and research, design the pipeline with these stages. For platform-specific triggers, caching, and syntax conventions, see references/platform-patterns.md.

Standard Stages (always include):

1. Lint & Format Check

• Run linter discovered in Phase 1 (e.g., ruff check, eslint, golangci-lint)
• Run formatter check if available (e.g., ruff format --check, prettier --check)
• Run type checker if applicable (e.g., pyright, tsc --noEmit, mypy)

2. Test

• Run test suite with discovered test command
• Include coverage reporting if configured
• Set up service containers if tests require databases/caches
• Consider matrix testing for multiple runtime versions

3. Build

• Run build command if applicable (e.g., npm run build, cargo build --release)
• Build Docker image if Dockerfile exists
• Generate artifacts for deployment

4. Security Scan

• Dependency vulnerability scanning (language-appropriate tool)
• Static analysis if available for the language
• Container scanning if Docker is used
• Secret detection

5. Deploy (if --deploy specified or deployment config detected)

• Environment-specific deployment steps
• Staging/production separation
• Post-deployment health checks
• Thread the built artifact reference into the deploy step. The Build stage must expose the image tag/digest it just produced (job outputs, GITHUB_OUTPUT, an artifact file, etc.), and the deploy step must consume that same reference — rendering it into a task definition, helm upgrade --set image.tag=<ref>, kubectl set image deployment/<name> <container>=<ref>, or equivalent. Never emit a blind restart (aws ecs update-service --force-new-deployment, kubectl rollout restart with no image change) as the whole deploy step: if the target pins an image tag/digest, a blind restart just re-pulls the OLD image and ships nothing new.

Design Decisions:

• Parallelism: Lint, test, and security scan run in parallel when possible
• Fail fast: Lint stage runs first (fastest feedback)
• Caching: Cache dependency installation for faster runs
• Branch strategy: Main/master triggers deploy; PRs trigger lint+test+build
• Artifacts: Build outputs passed between stages where needed

Phase 4: Generate Pipeline Configuration

Generate the complete CI/CD configuration file based on the designed architecture.

For platform-specific syntax and patterns, consult:

• references/platform-patterns.md - Detailed YAML patterns for each platform

File Locations by Platform:

Platform	File Path
GitHub Actions	.github/workflows/ci.yml
GitLab CI	.gitlab-ci.yml
CircleCI	.circleci/config.yml
Jenkins	Jenkinsfile

Generation Guidelines:

1. Use discovered commands exactly (do not invent scripts)
2. Pin action/orb/image versions to specific tags (not latest)
3. Include inline comments explaining non-obvious configuration
4. Set appropriate timeouts for each job
5. Use environment variables for configurable values
6. Follow the platform's recommended project structure

If an existing CI config exists:

• Read the existing file first
• Ask the user whether to replace or augment
• Preserve any custom configuration the user has added
• Merge new stages with existing ones where appropriate

Phase 5: Validate & Present

Step 5.1: Syntax Validation

Validate the generated configuration:

# Any YAML-based platform (GitHub Actions, GitLab CI, CircleCI) — parse the generated file
python3 -c "import yaml; yaml.safe_load(open('<config_file>'))"

# GitLab CI — prefer the project's own linter if available
gitlab-ci-lint .gitlab-ci.yml 2>/dev/null || python3 -c "import yaml; yaml.safe_load(open('.gitlab-ci.yml'))"

# CircleCI — prefer the project's own CLI if available
circleci config validate 2>/dev/null || python3 -c "import yaml; yaml.safe_load(open('.circleci/config.yml'))"

# Jenkins — Jenkinsfile is Groovy, not YAML; no local parser available, so
# just re-read the generated file for obvious syntax errors (unbalanced
# braces/quotes) instead of skipping validation

Step 5.2: Cross-Reference Check

Verify all referenced commands and paths exist:

1. Every script/command in the pipeline exists in the project
2. Every referenced file path is valid
3. Service versions match project requirements
4. Environment variable names are consistent
5. If a deploy stage exists, its deploy step references the image tag/digest (or equivalent build artifact) produced by the Build stage — not a blind restart with no reference to what was just built

Step 5.3: Present Summary

Output a summary including:

• Pipeline architecture overview
• Stages and their purposes
• Trigger conditions (which branches, PR events)
• Required secrets/environment variables to configure
• Cache strategy explanation
• Any manual steps needed (e.g., setting up deployment secrets)

Do not estimate run time per stage — it hasn't run yet and any number would be a guess.

Handling Ambiguity

If encountering unclear requirements:

1. Ask the user to clarify platform choice, deployment target, or branching strategy.
2. Present options with trade-offs when multiple valid approaches exist.
3. Default to the most common configuration for the detected stack.

Usage Examples

# Auto-detect everything
ci-generate

# Specify platform
ci-generate github
ci-generate gitlab
ci-generate circleci
ci-generate jenkins

# With deployment target
ci-generate github --deploy vercel
ci-generate gitlab --deploy docker
ci-generate github --deploy aws

# Monorepo support
ci-generate github --monorepo

# Combined options
ci-generate github --deploy k8s --monorepo

Important Notes

• Discover first: Never assume project tooling; always run Phase 1
• Pin versions: Use specific versions for actions, orbs, images, and tools
• Secrets documentation: List all required secrets so users know what to configure
• Existing config: Always check for and respect existing CI configuration
• Security by default: Include dependency scanning and secret detection in every pipeline
• Cache effectively: Proper caching can reduce CI times by 50-80%