nw-cicd-and-deployment
This Claude Code skill provides CI/CD pipeline design methodology covering local quality gates, multi-stage pipeline architecture, and deployment strategies. Use it when designing new CI/CD workflows, establishing GitHub Actions patterns, defining branch strategies, or implementing deployment gates to ensure fast feedback loops and production safety.
git clone --depth 1 https://github.com/nWave-ai/nWave /tmp/nw-cicd-and-deployment && cp -r /tmp/nw-cicd-and-deployment/nWave/skills/nw-cicd-and-deployment ~/.claude/skills/nw-cicd-and-deploymentSKILL.md
# CI/CD Pipeline Design and Deployment Strategies
## Local Quality Gates
Catch issues at the developer's machine before they reach CI. Local gates mirror the remote commit stage for fast feedback (seconds vs minutes).
### Gate Taxonomy
| Gate | Trigger | Checks | Tools |
|------|---------|--------|-------|
| Pre-commit | `git commit` | Formatting, linting, unit tests, secrets scan | pre-commit, husky, lefthook |
| Pre-push | `git push` | Integration tests, acceptance tests, coverage threshold | pre-commit (push stage), git hooks |
| Local CI | Manual | Full pipeline locally | act (GitHub Actions), gitlab-runner exec |
### Design Principles
- **Mirror, not duplicate**: local gates run the same checks as the remote commit stage, not additional ones. Keeps developer experience consistent with CI.
- **Fast by default**: pre-commit gates target < 30 seconds. Move slow checks (integration, acceptance) to pre-push.
- **Escapable with audit trail**: allow `--no-verify` for emergencies but log skips. CI remains the authoritative gate.
- **Framework selection**: prefer `pre-commit` (Python ecosystem) or `lefthook` (polyglot, fast parallel execution) over raw git hooks. Husky for JS/TS-heavy projects.
### Hook Stage Assignment
```
pre-commit (< 30s): formatting | linting | unit tests (fast subset) | secrets scan
pre-push (< 5 min): full unit suite | integration tests | coverage check | type checking
```
## Pipeline Stages
### Commit Stage (target: < 10 minutes)
Compile/build | Run unit tests (fast, isolated) | Static code analysis (linting, formatting) | Security scanning (SAST, secrets detection) | Generate build artifacts.
Quality gates: build success | 100% unit test pass rate | coverage threshold (e.g., > 80%) | no critical vulnerabilities | no secrets in code.
### Acceptance Stage (target: < 30 minutes)
Deploy to test environment | Run acceptance/integration/contract tests | Security scanning (DAST).
Quality gates: 100% acceptance/integration pass rate | no high/critical security findings | API contracts validated.
### Capacity Stage (target: < 60 minutes, can run parallel)
Performance, load, and stress testing | Chaos engineering experiments.
Quality gates: performance within SLO thresholds | load test pass (expected traffic + margin) | resilience under failure.
### Production Stage
Progressive deployment (canary/blue-green) | Health checks and smoke tests | SLO monitoring during rollout | Automatic rollback on degradation.
Quality gates: health checks pass | SLOs maintained | no error rate increase | latency within bounds.
## Quality Gate Classification
Every quality gate has a category (where it runs), a type (what happens on failure), and a scope (what it protects).
### Gate Taxonomy
| Category | Stage | Type | Examples |
|----------|-------|------|----------|
| Local | Pre-commit, pre-push | Blocking (developer) | Format, lint, unit tests, secrets scan |
| PR | Pull request | Blocking (merge) | Status checks, review approvals, coverage diff |
| CI | Commit stage | Blocking (pipeline) | Build, unit tests, SAST, coverage threshold |
| CI | Acceptance stage | Blocking (pipeline) | Integration, acceptance, contract tests, DAST |
| Deploy | Environment promotion | Blocking (approval) | Manual approval, change advisory board |
| Deploy | Canary/progressive | Automatic (rollback) | Error rate, latency, SLO breach |
| Production | Post-deploy | Advisory (monitoring) | Smoke tests, SLO monitoring window, business metrics |
### Gate Types
- **Blocking**: pipeline halts on failure. Merge/deploy/promotion is prevented until resolved.
- **Automatic (rollback)**: no human intervention -- system rolls back on threshold breach. Requires pre-defined thresholds and rollback automation.
- **Advisory**: failure is reported but does not block. Used for post-deploy monitoring where rollback is a separate decision.
### Design Checklist
When designing quality gates for a pipeline, verify:
1. Every remote CI gate has a local equivalent (pre-commit or pre-push)
2. PR gates include both automated checks (status checks) and human review (approvals)
3. Deployment gates distinguish blocking (promotion) from automatic (canary rollback)
4. Post-deploy gates have clear escalation paths (advisory -> manual rollback decision)
5. Gate thresholds are documented and versioned (not hardcoded in pipeline YAML)
## GitHub Actions Patterns
### Workflow Structure
Triggers: push to main/develop | pull_request | release tags | manual workflow_dispatch.
Jobs flow: build -> security -> deploy_staging -> deploy_production. Each with appropriate `needs` dependencies and environment gates.
### Quality Gate Pattern
```yaml
- name: Quality Gate
run: |
COVERAGE=$(jq '.totals.percent_covered' coverage.json)
if (( $(echo "$COVERAGE < 80" | bc -l) )); then
echo "Coverage $COVERAGE% is below 80% threshold"
exit 1
fi
```
### Caching Pattern
```yaml
- uses: actions/cache@v4
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
restore-keys: |
${{ runner.os }}-pip-
```
### Matrix Testing Pattern
```yaml
strategy:
matrix:
python-version: ['3.10', '3.11', '3.12']
os: [ubuntu-latest, macos-latest]
```
## Deployment Strategies
### Rolling Deployment
Gradual replacement of instances. Kubernetes config: `type: RollingUpdate`, `maxSurge: 25%`, `maxUnavailable: 0`.
- Pros: zero downtime | simple | efficient resources
- Cons: slow rollback | mixed versions during deployment
- Use when: stateless services | no breaking API changes | low-risk changes
### Blue-Green Deployment
Two identical environments, instant switch: Blue (current) serves traffic -> Deploy new to Green -> Smoke tests on Green -> Switch load balancer -> Blue becomes standby/rollback.
- Pros: instant rollback | easy pre-switch testing | clean version separation
- Cons: requires 2x resources | database migrations need care
- Use when: instant rollback needed | critReview dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance
Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating architecture styles.
Comprehensive architecture patterns, methodologies, quality frameworks, and evaluation methods for solution architects. Load when designing system architecture or selecting patterns.
Canonical AT completeness gate — research-anchored 7-category taxonomy (C1-C7) + 15-item mechanical checklist. Paradigm-neutral. Drives acceptance-designer reviewer verdict deterministically.