gitlab-ci-patterns▌
wshobson/agents · updated Apr 8, 2026
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Multi-stage GitLab CI/CD pipelines with Docker builds, Kubernetes deployments, and security scanning.
- ›Covers core pipeline patterns including build, test, and deploy stages with artifact caching and environment management
- ›Includes Docker image building and pushing to registries, multi-environment deployments (staging/production), and Terraform infrastructure automation
- ›Provides security scanning templates (SAST, dependency scanning, container scanning) and Trivy vulnerability checks
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
Terraform Pipeline
stages:
- validate
- plan
- apply
variables:
TF_ROOT: ${CI_PROJECT_DIR}/terraform
TF_VERSION: "1.6.0"
before_script:
- cd ${TF_ROOT}
- terraform --version
validate:
stage: validate
image: hashicorp/terraform:${TF_VERSION}
script:
- terraform init -backend=false
- terraform validate
- terraform fmt -check
plan:
stage: plan
image: hashicorp/terraform:${TF_VERSION}
script:
- terraform init
- terraform plan -out=tfplan
artifacts:
paths:
- ${TF_ROOT}/tfplan
expire_in: 1 day
apply:
stage: apply
image: hashicorp/terraform:${TF_VERSION}
script:
- terraform init
- terraform apply -auto-approve tfplan
dependencies:
- plan
when: manual
only:
- main
Security Scanning
include:
- template: Security/SAST.gitlab-ci.yml
- template: Security/Dependency-Scanning.gitlab-ci.yml
- template: Security/Container-Scanning.gitlab-ci.yml
trivy-scan:
stage: test
image: aquasec/trivy:latest
script:
- trivy image --exit-code 1 --severity HIGH,CRITICAL $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
allow_failure: true
Caching Strategies
# Cache node_modules
build:
cache:
key: ${CI_COMMIT_REF_SLUG}
paths:
- node_modules/
policy: pull-push
# Global cache
cache:
key: ${CI_COMMIT_REF_SLUG}
paths:
- .cache/
- vendor/
# Separate cache per job
job1:
cache:
key: job1-cache
paths:
- build/
how to use gitlab-ci-patternsHow to use gitlab-ci-patterns on Cursor
AI-first code editor with Composer
1Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add gitlab-ci-patterns
2Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/wshobson/agents --skill gitlab-ci-patternsThe skills CLI fetches gitlab-ci-patterns from GitHub repository wshobson/agents and configures it for Cursor.
3Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?││ ── Universal (.agents/skills) ── always included ────│ • Amp│ • Antigravity│ • Cline│ • Codex│ ●Cursor(selected)│ • Cursor│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/gitlab-ci-patternsReload or restart Cursor to activate gitlab-ci-patterns. Access the skill through slash commands (e.g., /gitlab-ci-patterns) or your agent's skill management interface.
⚠Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
List & Monetize Your Skill
Submit your Claude Code skill and start earning
GET_STARTED →Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
✓Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
✓Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.5★★★★★52 reviews- ★★★★★Dhruvi Jain· Dec 24, 2024
gitlab-ci-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anaya Liu· Dec 24, 2024
Keeps context tight: gitlab-ci-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sophia Farah· Dec 8, 2024
We added gitlab-ci-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Dec 4, 2024
Solid pick for teams standardizing on skills: gitlab-ci-patterns is focused, and the summary matches what you get after install.
- ★★★★★Li Mensah· Nov 27, 2024
Keeps context tight: gitlab-ci-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Advait Choi· Nov 27, 2024
Solid pick for teams standardizing on skills: gitlab-ci-patterns is focused, and the summary matches what you get after install.
- ★★★★★Oshnikdeep· Nov 15, 2024
I recommend gitlab-ci-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Anaya Taylor· Nov 15, 2024
We added gitlab-ci-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Noah Kim· Oct 18, 2024
gitlab-ci-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kwame Agarwal· Oct 18, 2024
gitlab-ci-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
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