DevOps & Infra Skill
Bash Defensive Patterns
Master defensive Bash programming techniques for production-grade scripts.
$15
GitLab CI Patterns
Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for…
A pattern library for building scalable GitLab CI/CD pipelines with multi-stage workflows, smart caching, and distributed runner autoscaling. It uses DAG-based parallelism via the needs keyword, branch-isolated cache strategies, and merge-request pipelines to cut pipeline time and runner cost. Includes ready templates for Docker build, multi-environment deploy, Terraform, security scanning, and dynamic child pipelines.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category DevOps & Infra
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
The pipeline assembly the skill performs in .gitlab-ci.yml, from skeleton to hardened production flow:
- Lays the stage skeleton: build, test, deploy stages with artifacts carrying build output between jobs under explicit expire_in windows, and coverage regex wired so the coverage number lands in the merge request widget.
- Converts sequential stages into a DAG: independent jobs declare needs on build and run in parallel instead of waiting for their stage, which routinely cuts a ten-minute pipeline to six; interruptible true cancels stale pipelines when a new push lands on the same MR.
- Tunes the cache policy: keys scoped by CI_COMMIT_REF_SLUG for branch isolation, pull-push only on the job that writes the cache and pull everywhere else, plus lock-file based keys so node_modules only re-downloads when the lockfile actually changes.
- Builds Docker images with dind: registry login from CI variables, images tagged with CI_COMMIT_SHA for immutability, pushed only on main and tags.
- Splits deployment per environment with a shared template anchor: staging deploys automatically from develop, production requires when manual on main, and the environment keyword gives GitLab a tracked deploy history with rollback.
- Includes the security templates: SAST, Dependency Scanning and Container Scanning from GitLab's catalog, plus a Trivy job with exit-code 1 on HIGH and CRITICAL findings so a known-vulnerable image cannot reach the registry quietly.
One power source. 6 lines out.
gitlab-ci-patterns · core
core active · 6 lines
- ✓ building a multi-stage b…
Building a multi-stage build-test-deploy pipeline in .gitlab-ci.yml
- ✓ parallelizing independen…
Parallelizing independent jobs with needs to shorten total pipeline time
- ✓ configuring branch-isola…
Configuring branch-isolated caching for node_modules and dependencies
- ✓ deploying to staging and
Deploying to staging and production Kubernetes with manual gates
- ✓ running a terraform vali…
Running a Terraform validate-plan-apply pipeline with manual apply
- ✓ adding sast, dependency
Adding SAST, dependency, and container scanning via GitLab templates
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
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Cut pipeline duration with DAG parallelism that runs independent jobs at once
license: perpetual -
Slash runner cost: autoscaling drops idle runners to zero off-peak
license: perpetual -
Avoid stale or wasteful caches with branch-keyed, lock-file-aware cache policy
license: perpetual -
Catch issues before merge with shift-left merge-request pipelines
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
Basic build-test-deploy pipeline with artifacts and coverage reporting
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
DevOps engineers and platform teams running GitLab who want fast, cost-efficient, secure pipelines instead of slow sequential stages.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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We run self-hosted GitLab CE, do the patterns still apply?
The core patterns are plain .gitlab-ci.yml: DAG parallelism with needs, branch-keyed caching, and the multi-stage templates all work on CE. Runner autoscaling assumes you operate your own runner fleet.
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How does it actually cut pipeline time?
Independent jobs run simultaneously via the needs keyword instead of waiting for whole stages, and lock-file-aware caching stops every job from reinstalling dependencies. Merge-request pipelines then catch failures before code ever merges.
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Can I reuse these for my GitHub Actions workflows?
No. The patterns are tied to GitLab CI syntax and its runner model. For the GitHub side, the sibling GitHub Actions template library is the right product.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.