Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/engineerwithai/engineerwith-agents/gitlab-ci-patternsnpx skills add EngineerWithAI/engineerwith-agents --skill gitlab-ci-patternsgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00046 | $0.01494 |
| Opus 5 | $0.00023 | $0.00747 |
| Sonnet 5 | $0.00009 | $0.00299 |
| Haiku 4.5 | $0.00005 | $0.00149 |
Grade A, and why
gitlab-ci-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to gitlab-ci-patterns — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitLab CI Patterns
Comprehensive GitLab CI/CD pipeline patterns for automated testing, building, and deployment.
Purpose
Create efficient GitLab CI pipelines with proper stage organization, caching, and deployment strategies.
When to Use
- Automate GitLab-based CI/CD
- Implement multi-stage pipelines
- Configure GitLab Runners
- Deploy to Kubernetes from GitLab
- Implement GitOps workflows
Basic Pipeline Structure
stages:
- build
- test
- deploy
variables:
DOCKER_DRIVER: overlay2
DOCKER_TLS_CERTDIR: "/certs"
build:
stage: build
image: node:20
script:
- npm ci
- npm run build
artifacts:
paths:
- dist/
expire_in: 1 hour
cache:
key: ${CI_COMMIT_REF_SLUG}
paths:
- node_modules/
test:
stage: test
image: node:20
script:
- npm ci
- npm run lint
- npm test
coverage: '/Lines\s*:\s*(\d+\.\d+)%/'
artifacts:
reports:
coverage_report:
coverage_format: cobertura
path: coverage/cobertura-coverage.xml
deploy:
stage: deploy
image: bitnami/kubectl:latest
script:
- kubectl apply -f k8s/
- kubectl rollout status deployment/my-app
only:
- main
environment:
name: production
url: https://app.example.com
Docker Build and Push
build-docker:
stage: build
image: docker:24
services:
- docker:24-dind
before_script:
- docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
script:
- docker build -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA .
- docker build -t $CI_REGISTRY_IMAGE:latest .
- docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
- docker push $CI_REGISTRY_IMAGE:latest
only:
- main
- tags
Multi-Environment Deployment
.deploy_template: &deploy_template
image: bitnami/kubectl:latest
before_script:
- kubectl config set-cluster k8s --server="$KUBE_URL" --insecure-skip-tls-verify=true
- kubectl config set-credentials admin --token="$KUBE_TOKEN"
- kubectl config set-context default --cluster=k8s --user=admin
- kubectl config use-context default
deploy:staging:
<<: *deploy_template
stage: deploy
script:
- kubectl apply -f k8s/ -n staging
- kubectl rollout status deployment/my-app -n staging
environment:
name: staging
url: https://staging.example.com
only:
- develop
deploy:production:
<<: *deploy_template
stage: deploy
script:
- kubectl apply -f k8s/ -n production
- kubectl rollout status deployment/my-app -n production
environment:
name: production
url: https://app.example.com
when: manual
only:
- main
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 272 lines · 46 tokens per session scan A 109b4492fc27
gitlab-ci-patterns is a skill published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 46 tokens to every session and 1,494 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gitlab-ci-patterns, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.