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Gitlab Ci

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BagelHole
gitlab-ci

Configure GitLab CI/CD pipelines and runners for automated building, testing, and deployment. Create .gitlab-ci.yml configurations, manage runners, and implement DevOps workflows. Use when working with GitLab repositories or self-hosted GitLab instances.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill namegitlab-ci
Stars
1.1K
Forks
161
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by BagelHole on GitHub. Read the source before you install it.

Installation

Install the Gitlab Ci AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/devops/ci-cd/gitlab-ci
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/devops/ci-cd/gitlab-ci .claude/skills/gitlab-ci
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gitlab Ci in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gitlab Ci on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gitlab Ci is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GitLab CI/CD

Automate your software delivery pipeline with GitLab's integrated CI/CD system.

When to Use This Skill

Use this skill when:

  • Setting up CI/CD pipelines in GitLab
  • Configuring GitLab runners (shared or self-hosted)
  • Creating multi-stage deployment pipelines
  • Implementing GitLab Auto DevOps
  • Managing CI/CD variables and secrets

Prerequisites

  • GitLab repository (gitlab.com or self-hosted)
  • Basic understanding of YAML
  • For self-hosted runners: Linux server or Kubernetes cluster

Pipeline Configuration

Create .gitlab-ci.yml in repository root:

yaml
stages:
  - build
  - test
  - deploy

variables:
  NODE_VERSION: "20"

build:
  stage: build
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - npm run build
  artifacts:
    paths:
      - dist/
    expire_in: 1 hour

test:
  stage: test
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - npm test
  coverage: '/Coverage: \d+\.\d+%/'

deploy:
  stage: deploy
  script:
    - ./deploy.sh
  environment:
    name: production
    url: https://example.com
  only:
    - main

Job Configuration

Rules-Based Execution

yaml
deploy:
  script: ./deploy.sh
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
      when: manual
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"
      when: never
    - when: on_success

Parallel Jobs

yaml
test:
  stage: test
  parallel: 3
  script:
    - npm test -- --shard=$CI_NODE_INDEX/$CI_NODE_TOTAL

Matrix Builds

yaml
test:
  stage: test
  parallel:
    matrix:
      - NODE_VERSION: ["18", "20", "22"]
        OS: ["alpine", "slim"]
  image: node:${NODE_VERSION}-${OS}
  script:
    - npm test

Caching

yaml
cache:
  key:
    files:
      - package-lock.json
  paths:
    - node_modules/
  policy: pull-push

build:
  cache:
    key: build-cache
    paths:
      - .cache/
    policy: pull

Artifacts

yaml
build:
  artifacts:
    paths:
      - dist/
      - coverage/
    reports:
      junit: junit.xml
      coverage_report:
        coverage_format: cobertura
        path: coverage/cobertura.xml
    expire_in: 1 week
    when: always

Environments and Deployments

yaml
deploy_staging:
  stage: deploy
  script:
    - deploy --env staging
  environment:
    name: staging
    url: https://staging.example.com
    on_stop: stop_staging

stop_staging:
  stage: deploy
  script:
    - undeploy --env staging
  environment:
    name: staging
    action: stop
  when: manual

Docker Builds

yaml
build_image:
  stage: build
  image: docker:24
  services:
    - docker:24-dind
  variables:
    DOCKER_TLS_CERTDIR: "/certs"
  script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    - docker build -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA .
    - docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA

GitLab Runners

Install Runner

bash
# Download and install
curl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh | sudo bash
sudo apt install gitlab-runner

# Register runner
sudo gitlab-runner register \
  --url https://gitlab.com/ \
  --registration-token TOKEN \
  --executor docker \
  --docker-image alpine:latest

Runner Configuration

toml
# /etc/gitlab-runner/config.toml
[[runners]]
  name = "docker-runner"
  url = "https://gitlab.com/"
  token = "TOKEN"
  executor = "docker"
  [runners.docker]
    image = "alpine:latest"
    privileged = true
    volumes = ["/cache", "/var/run/docker.sock:/var/run/docker.sock"]

Runner Tags

yaml
build:
  tags:
    - docker
    - linux
  script:
    - make build

CI/CD Variables

Protected Variables

Define in Settings > CI/CD > Variables:

  • AWS_ACCESS_KEY_ID (protected, masked)
  • AWS_SECRET_ACCESS_KEY (protected, masked)

Using Variables

yaml
deploy:
  script:
    - aws s3 sync dist/ s3://$S3_BUCKET
  variables:
    AWS_DEFAULT_REGION: us-east-1

Include and Extend

Include Templates

yaml
include:
  - template: Security/SAST.gitlab-ci.yml
  - project: 'group/shared-ci'
    file: '/templates/deploy.yml'
  - local: '/ci/jobs.yml'

Extend Jobs

yaml
.base_job:
  image: node:20
  before_script:
    - npm ci

build:
  extends: .base_job
  script:
    - npm run build

test:
  extends: .base_job
  script:
    - npm test

Multi-Project Pipelines

yaml
trigger_downstream:
  stage: deploy
  trigger:
    project: group/downstream-project
    branch: main
    strategy: depend

Common Issues

Issue: Pipeline Stuck

Problem: Jobs stay pending Solution: Check runner availability and tags matching

Issue: Docker-in-Docker Fails

Problem: Cannot connect to Docker daemon Solution: Use docker:dind service with proper TLS configuration

Issue: Cache Not Working

Problem: Cache misses between jobs Solution: Verify cache key and ensure runners share distributed cache

Best Practices

  • Use rules instead of only/except for complex conditions
  • Leverage GitLab's built-in security scanning templates
  • Use job dependencies to optimize pipeline speed
  • Implement review apps for merge requests
  • Cache dependencies aggressively
  • Use artifacts for passing data between stages

Related Skills

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gitlab Ci AI skill do?

Configure GitLab CI/CD pipelines and runners for automated building, testing, and deployment. Create .gitlab-ci.yml configurations, manage runners, and implement DevOps workflows. Use when working with GitLab repositories or self-hosted GitLab instances.

Why use Gitlab Ci on TypingMind?

Because you install it once and use it with any model. Gitlab Ci is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gitlab Ci in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/devops/ci-cd/gitlab-ci. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gitlab Ci?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gitlab Ci?

As many as you like. As long as a model supports skills, you can use Gitlab Ci with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gitlab Ci AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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