ci-pipeline
Skill7 starsChanged 7 months agoMITView on GitHub
Generates a CI/CD pipeline configuration for the project. Auto-detects the stack and creates GitHub Actions, GitLab CI, or other CI configs with lint, test, build, and deploy stages.
5 other projects use a version of this.
Adopted by
Most recently taken up by
- LeanMeanCleanApps/CleanModelContextProtocoltheir own version, first seen Nov 8, 2026
- Ionate-Technologies/indium-agentkittheir own version, first seen Oct 20, 2026
- Indium-AI-Labs/indium-agentkittheir own version, first seen Oct 11, 2026
- Jinbae10/skillstheir own version, first seen Jul 7, 2026
- vndee/engineering-skillstheir own version, first seen Mar 22, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- Indium-AI-Labs/indium-agentkit★ 18same name
- Ionate-Technologies/indium-agentkit★ 17same name
- vndee/engineering-skills★ 3same name
- LeanMeanCleanApps/CleanModelContextProtocol★ 1same name
- Jinbae10/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository45 other files it gives its agents.
- .claude/CLAUDE.mdCLAUDE.md
- CLAUDE.mdCLAUDE.md
- a11y-auditSkill
- api-designSkill
- architecture-reviewSkill
- business-blueprintSkill
- business-logic-auditSkill
- changelogSkill
- code-coverageSkill
- competitive-analysisSkill
- convert-codeSkill
- create-componentSkill
- db-migrationSkill
- debugSkill
- dependency-auditSkill
- deploy-checklistSkill
- design-flowSkill
- docker-setupSkill
- env-setupSkill
- error-monitorSkill
- estimateSkill
- explain-codeSkill
- fix-issueSkill
- generate-docsSkill
- git-cleanupSkill
- grill-meSkill
- handoffSkill
- legal-pagesSkill
- memory-importSkill
- memorySkill
- microcopySkill
- migrate-frameworkSkill
- onboardSkill
- optimizeSkill
- pr-summarySkill
- refactorSkill
- reference-appSkill
- review-prSkill
- security-scanSkill
- seo-scaffoldSkill
- tddSkill
- web-launch-checkSkill
- wireframeSkill
- write-skillSkill
- write-testsSkill
The file
We split this skill into the rules it gives an agent
11 rules. 0 are common, 11 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
11
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 32
Use lock file hash as cache key
- Distinctive: line 35
Run linter (ESLint, ruff, golangci-lint, clippy, etc.)
- Distinctive: line 36
Run type checker if applicable (tsc, mypy, go vet)
- Distinctive: line 37
Run formatter check (prettier, black, gofmt)
- Distinctive: line 40
Run unit tests with coverage
- Distinctive: line 41
Run integration tests (with service containers if needed)
- Distinctive: line 46
Verify build artifacts are created
- Distinctive: line 83
Always include a lint stage — catch issues early
- Distinctive: line 84
Always include caching — speed up repeat runs
- Distinctive: line 85
Keep the pipeline under 10 minutes for PRs
- Distinctive: line 86
Don't hardcode versions — use variables or matrix
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
CI Pipeline Generator
Detect Project Stack
!ls package.json requirements.txt pyproject.toml go.mod Cargo.toml *.csproj *.sln Gemfile composer.json mix.exs Makefile Dockerfile 2>/dev/null
!ls .github/workflows/ .gitlab-ci.yml Jenkinsfile .circleci/ .travis.yml bitbucket-pipelines.yml 2>/dev/null
!cat package.json 2>/dev/null | grep -E '"(scripts|devDependencies)"' -A 10 | head -20
Pipeline Design
Step 1: Detect Requirements
From project files, determine:
- Language and version (Node 20, Python 3.12, Go 1.22, etc.)
- Package manager (npm, pnpm, yarn, pip, poetry, cargo, etc.)
- Test command
- Lint command
- Build command
- Required services (database, Redis, etc.)
Step 2: Generate Pipeline Stages
Stage 1: Install
- Cache dependencies for fast subsequent runs
- Use lock file hash as cache key
Stage 2: Lint
- Run linter (ESLint, ruff, golangci-lint, clippy, etc.)
- Run type checker if applicable (tsc, mypy, go vet)
- Run formatter check (prettier, black, gofmt)
Stage 3: Test
- Run unit tests with coverage
- Run integration tests (with service containers if needed)
- Upload coverage report
Stage 4: Build
- Production build
- Verify build artifacts are created
Stage 5: Security (optional)
- Dependency vulnerability scan
- Secret scanning
- SAST if available
Stage 6: Deploy (placeholder)
- Staging deployment (on push to main)
- Production deployment (on tag/release)
- Mark as manual/approval required
Step 3: Platform-Specific Output
GitHub Actions:
name: CI
on: [push, pull_request]
jobs:
ci:
runs-on: ubuntu-latest
steps: ...GitLab CI:
stages: [install, lint, test, build, deploy]Step 4: Optimizations
- Dependency caching (npm cache, pip cache, cargo cache)
- Parallel test execution where possible
- Matrix builds for multiple versions (if needed)
- Fail-fast on lint errors (don't waste time on tests)
- Artifact upload for build output
Rules
- Always include a lint stage — catch issues early
- Always include caching — speed up repeat runs
- Keep the pipeline under 10 minutes for PRs
- Don't hardcode versions — use variables or matrix
- Include both push and PR triggers
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

