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Circleci

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BagelHole
circleci

Configure CircleCI workflows and orbs for continuous integration and deployment. Create config.yml pipelines, use orbs for reusable configurations, and optimize build performance. Use when working with CircleCI for CI/CD automation.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill namecircleci
Stars
1.1K
Forks
161
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Circleci 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/circleci
  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/circleci .claude/skills/circleci
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Circleci 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 Circleci 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 Circleci 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.

CircleCI

Build, test, and deploy applications using CircleCI's cloud-native CI/CD platform.

When to Use This Skill

Use this skill when:

  • Setting up CI/CD pipelines with CircleCI
  • Using orbs for reusable configuration
  • Optimizing build times with caching and parallelism
  • Configuring CircleCI workflows and approvals
  • Managing CircleCI contexts and secrets

Prerequisites

  • CircleCI account connected to repository
  • Project enabled in CircleCI dashboard
  • Basic YAML understanding

Configuration File

Create .circleci/config.yml:

yaml
version: 2.1

orbs:
  node: circleci/node@5.2
  docker: circleci/docker@2.4

executors:
  default:
    docker:
      - image: cimg/node:20.10
    working_directory: ~/project

jobs:
  build:
    executor: default
    steps:
      - checkout
      - node/install-packages:
          pkg-manager: npm
      - run:
          name: Build application
          command: npm run build
      - persist_to_workspace:
          root: .
          paths:
            - dist

  test:
    executor: default
    steps:
      - checkout
      - node/install-packages:
          pkg-manager: npm
      - run:
          name: Run tests
          command: npm test

  deploy:
    executor: default
    steps:
      - checkout
      - attach_workspace:
          at: .
      - run:
          name: Deploy
          command: ./deploy.sh

workflows:
  build-test-deploy:
    jobs:
      - build
      - test:
          requires:
            - build
      - deploy:
          requires:
            - test
          filters:
            branches:
              only: main

Executors

Docker Executor

yaml
executors:
  node:
    docker:
      - image: cimg/node:20.10
      - image: cimg/postgres:15.0
        environment:
          POSTGRES_USER: test
          POSTGRES_DB: testdb
    working_directory: ~/app

Machine Executor

yaml
executors:
  linux-machine:
    machine:
      image: ubuntu-2204:current
    resource_class: large

macOS Executor

yaml
executors:
  macos:
    macos:
      xcode: "15.0.0"
    resource_class: macos.m1.medium.gen1

Caching

Dependency Caching

yaml
jobs:
  build:
    steps:
      - checkout
      - restore_cache:
          keys:
            - v1-deps-{{ checksum "package-lock.json" }}
            - v1-deps-
      - run: npm ci
      - save_cache:
          key: v1-deps-{{ checksum "package-lock.json" }}
          paths:
            - node_modules

Multi-Key Caching

yaml
- restore_cache:
    keys:
      - v1-{{ .Branch }}-{{ checksum "package-lock.json" }}
      - v1-{{ .Branch }}-
      - v1-main-
      - v1-

Workspaces

Persist Data

yaml
jobs:
  build:
    steps:
      - checkout
      - run: npm run build
      - persist_to_workspace:
          root: .
          paths:
            - dist
            - node_modules

  deploy:
    steps:
      - attach_workspace:
          at: ~/project
      - run: ./deploy.sh

Parallelism

Test Splitting

yaml
jobs:
  test:
    parallelism: 4
    steps:
      - checkout
      - run:
          name: Run tests
          command: |
            TESTFILES=$(circleci tests glob "test/**/*.test.js" | circleci tests split --split-by=timings)
            npm test -- $TESTFILES
      - store_test_results:
          path: test-results

Workflows

Sequential Jobs

yaml
workflows:
  pipeline:
    jobs:
      - build
      - test:
          requires:
            - build
      - deploy:
          requires:
            - test

Parallel Jobs

yaml
workflows:
  pipeline:
    jobs:
      - build
      - test-unit:
          requires:
            - build
      - test-integration:
          requires:
            - build
      - deploy:
          requires:
            - test-unit
            - test-integration

Manual Approval

yaml
workflows:
  deploy-prod:
    jobs:
      - build
      - test
      - hold:
          type: approval
          requires:
            - test
      - deploy-production:
          requires:
            - hold

Scheduled Workflows

yaml
workflows:
  nightly:
    triggers:
      - schedule:
          cron: "0 2 * * *"
          filters:
            branches:
              only:
                - main
    jobs:
      - build
      - test

Branch Filtering

yaml
workflows:
  build-deploy:
    jobs:
      - build:
          filters:
            branches:
              only:
                - main
                - /feature-.*/
      - deploy:
          filters:
            branches:
              only: main
            tags:
              only: /^v.*/

Orbs

Using Orbs

yaml
version: 2.1

orbs:
  aws-cli: circleci/aws-cli@4.1
  kubernetes: circleci/kubernetes@1.3

jobs:
  deploy:
    executor: aws-cli/default
    steps:
      - aws-cli/setup:
          aws_access_key_id: AWS_ACCESS_KEY_ID
          aws_secret_access_key: AWS_SECRET_ACCESS_KEY
      - kubernetes/install-kubectl
      - run: kubectl apply -f k8s/

Common Orbs

yaml
orbs:
  node: circleci/node@5.2              # Node.js
  docker: circleci/docker@2.4          # Docker builds
  aws-cli: circleci/aws-cli@4.1        # AWS CLI
  aws-ecr: circleci/aws-ecr@9.0        # ECR push
  aws-ecs: circleci/aws-ecs@4.0        # ECS deploy
  gcp-cli: circleci/gcp-cli@3.1        # GCP CLI
  kubernetes: circleci/kubernetes@1.3  # K8s deploy
  slack: circleci/slack@4.12           # Notifications

Docker Builds

yaml
version: 2.1

orbs:
  docker: circleci/docker@2.4

jobs:
  build-and-push:
    executor: docker/docker
    steps:
      - setup_remote_docker:
          version: 20.10.24
      - checkout
      - docker/check
      - docker/build:
          image: myorg/myapp
          tag: $CIRCLE_SHA1
      - docker/push:
          image: myorg/myapp
          tag: $CIRCLE_SHA1

Environment Variables

Project Variables

Set in CircleCI Project Settings > Environment Variables

Contexts

yaml
workflows:
  deploy:
    jobs:
      - deploy-staging:
          context: staging-secrets
      - deploy-production:
          context: production-secrets

Using Variables

yaml
jobs:
  deploy:
    steps:
      - run:
          name: Deploy
          command: |
            aws s3 sync dist/ s3://$S3_BUCKET
          environment:
            AWS_DEFAULT_REGION: us-east-1

Artifacts and Test Results

yaml
jobs:
  test:
    steps:
      - run:
          name: Run tests
          command: npm test -- --coverage
      - store_test_results:
          path: test-results
      - store_artifacts:
          path: coverage
          destination: coverage-report

Resource Classes

yaml
jobs:
  build:
    docker:
      - image: cimg/node:20.10
    resource_class: large  # 4 vCPU, 8GB RAM
    steps:
      - checkout
      - run: npm run build

# Available classes:
# small: 1 vCPU, 2GB RAM
# medium: 2 vCPU, 4GB RAM (default)
# large: 4 vCPU, 8GB RAM
# xlarge: 8 vCPU, 16GB RAM

Common Issues

Issue: Cache Not Restoring

Problem: Cache misses on every build Solution: Verify cache key format, ensure checksum file hasn't changed

Issue: Workspace Attach Fails

Problem: Cannot find persisted workspace Solution: Ensure persist_to_workspace job completed, check paths

Issue: Docker Layer Caching

Problem: Docker builds are slow Solution: Enable Docker Layer Caching in project settings (paid feature)

Best Practices

  • Use orbs for common tasks
  • Implement aggressive caching strategies
  • Use workspaces for sharing data between jobs
  • Split tests with parallelism for faster builds
  • Use contexts for environment-specific secrets
  • Define reusable executors
  • Store test results for insights

Related Skills

Frequently asked questions

What does the Circleci AI skill do?

Configure CircleCI workflows and orbs for continuous integration and deployment. Create config.yml pipelines, use orbs for reusable configurations, and optimize build performance. Use when working with CircleCI for CI/CD automation.

Why use Circleci on TypingMind?

Because you install it once and use it with any model. Circleci 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 Circleci 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/circleci. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Circleci?

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 Circleci?

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

Is the Circleci 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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