Generate GitHub Actions workflow YAML, analyze existing pipelines for optimization opportunities, and create deployment plans with strategy selection, health checks, and rollback procedures.
Core Capabilities
- CI pipeline design — fail-fast job ordering (lint → unit → build → integration → security) with matrix testing and CI time/flake/cache targets.
- CD & multi-environment — dev/staging/prod promotion flows, build-once-deploy-everywhere, environment protection rules, and rollback at every stage.
- Pipeline optimization — detect missing caching, missing timeouts, serial chains, deprecated actions, leaked secrets, and oversized runners; apply path filtering and concurrency cancellation.
- Deployment strategies — choose blue-green, canary, or rolling via decision tree; canary traffic-split schedule with promotion gates.
- GitHub Actions patterns — reusable workflows, OIDC auth, secrets hierarchy, and runner cost estimation.
When to Use
- Designing a new CI or CD workflow from scratch.
- Planning a multi-environment (dev/staging/prod) deployment.
- Optimizing an existing pipeline's cost or runtime.
- Implementing a blue-green, canary, or rolling deployment strategy.
Clarify First
Before generating the workflow, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Workflow type — CI, CD, release, or security-scan (sets
workflow_generator.py --type) - Stack — language and test framework (e.g. python/pytest) (drives the generated YAML steps via
--language/--test-framework) - Deployment strategy & environments — blue-green, canary, or rolling, and which of dev/staging/prod (drives the
deployment_planner.pyplan)
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.
Tools
| Tool | Purpose | Command |
|---|---|---|
workflow_generator.py |
Generate GitHub Actions YAML (ci, cd, release, security-scan, docs-check) | python scripts/workflow_generator.py --type ci --language python --test-framework pytest |
pipeline_analyzer.py |
Analyze workflows for optimization findings, cost estimates, severity ratings | python scripts/pipeline_analyzer.py .github/workflows/ --format json |
deployment_planner.py |
Generate a deployment plan with strategy, health checks, rollback | python scripts/deployment_planner.py --type webapp --environments dev,staging,prod --strategy canary |
All tools support --format json and --output/-o for file writing.
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/workflows-and-optimization.md — the CI / CD / optimization workflows with full YAML, deployment-strategy decision tree and canary schedule, GitHub Actions patterns, runner cost table, anti-patterns, and troubleshooting. Read when building or tuning a pipeline.
- references/github-actions-patterns.md — deep GitHub Actions pattern library. Read when authoring advanced workflow YAML.
- references/deployment-strategies.md — deep deployment strategy guide (blue-green, canary, rolling). Read when planning a release rollout.
- references/agentic-workflows-guide.md — agentic/automated workflow patterns. Read when wiring up AI-driven or autonomous pipeline steps.
Integration Points
| Skill | Integration |
|---|---|
release-orchestrator |
Release workflows align with versioning and changelog |
senior-devops |
Deployment strategies complement infra automation |
senior-secops |
Security scanning steps feed SecOps dashboards |
senior-qa |
CI quality gates map to QA acceptance criteria |
incident-commander |
Rollback procedures connect to incident playbooks |