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Get Started Free →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.
.claude/skills/borghei-ci-cd-pipeline-builder/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -50% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 73% | 0% |
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 |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 18,143 | 20,219 | +11% | 1 | 1 | 0% | 3,679 | 4,055 | +10% | 0 | 0 | — |
case-02 | fail→pass | 14,930 | 36,306 | +143% | 1 | 1 | 0% | 2,801 | 2,796 | -0% | 0 | 0 | — |
case-03 | fail→pass | 12,710 | 14,212 | +12% | 1 | 1 | 0% | 2,541 | 4,204 | +65% | 0 | 0 | — |
case-09 | fail→fail | 16,794 | 21,817 | +30% | 1 | 1 | 0% | 3,440 | 6,414 | +86% | 0 | 0 | — |
case-10 | fail→fail | 16,000 | 11,793 | -26% | 1 | 1 | 0% | 3,121 | 3,566 | +14% | 0 | 0 | — |
case-04 | fail→pass | 20,495 | 3,381 | -84% | 1 | 1 | 0% | 3,453 | 1,711 | -50% | 0 | 0 | — |
case-05 | fail→pass | 8,035 | 3,696 | -54% | 1 | 1 | 0% | 1,211 | 1,788 | +48% | 0 | 0 | — |
case-06 | fail→pass | 4,846 | 1,798 | -63% | 1 | 1 | 0% | 837 | 1,451 | +73% | 0 | 0 | — |
case-07 | fail→fail | 28,508 | 11,013 | -61% | 1 | 1 | 0% | 5,762 | 3,166 | -45% | 0 | 0 | — |
case-08 | fail→pass | 19,895 | 4,791 | -76% | 1 | 1 | 0% | 4,045 | 1,914 | -53% | 0 | 0 | — |
case-11 | fail→fail | 17,862 | 23,473 | +31% | 1 | 1 | 0% | 3,800 | 6,003 | +58% | 0 | 0 | — |
case-12 | fail→fail | 10,678 | 12,416 | +16% | 1 | 1 | 0% | 2,094 | 3,517 | +68% | 0 | 0 | — |
case-13 | fail→pass | 7,591 | 12,247 | +61% | 1 | 1 | 0% | 1,460 | 2,806 | +92% | 0 | 0 | — |
case-14 | fail→pass | 10,564 | 19,485 | +84% | 1 | 1 | 0% | 2,080 | 4,494 | +116% | 0 | 0 | — |
case-15 | fail→pass | 12,131 | 12,222 | +1% | 1 | 1 | 0% | 2,148 | 3,584 | +67% | 0 | 0 | — |
case-16 | pass→pass | 9,635 | 10,168 | +6% | 1 | 1 | 0% | 1,574 | 2,811 | +79% | 0 | 0 | — |
case-17 | pass→pass | 8,525 | 6,459 | -24% | 1 | 1 | 0% | 1,289 | 2,189 | +70% | 0 | 0 | — |
case-18 | pass→pass | 5,205 | 6,731 | +29% | 1 | 1 | 0% | 1,042 | 2,391 | +129% | 0 | 0 | — |
case-19 | pass→pass | 8,972 | 9,141 | +2% | 1 | 1 | 0% | 1,587 | 2,830 | +78% | 0 | 0 | — |
case-20 | fail→pass | 6,808 | 6,245 | -8% | 1 | 1 | 0% | 1,135 | 2,251 | +98% | 0 | 0 | — |
case-21 | fail→pass | 5,624 | 9,003 | +60% | 1 | 1 | 0% | 1,022 | 2,647 | +159% | 0 | 0 | — |
case-22 | pass→pass | 4,421 | 10,308 | +133% | 1 | 1 | 0% | 716 | 2,791 | +290% | 0 | 0 | — |
case-23 | fail→pass | 5,736 | 8,505 | +48% | 1 | 1 | 0% | 870 | 2,502 | +188% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +52 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.