Circleci Agent Skill

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.

2k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

511

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill circleci

The instruction itself

37 sections, as written by the author

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:

version: 2.1

orbs:
  node: circleci/[email protected]
  docker: circleci/[email protected]

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

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

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

macOS Executor

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

Caching

Dependency Caching

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

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

Workspaces

Persist Data

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

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

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

Parallel Jobs

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

Manual Approval

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

Scheduled Workflows

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

Branch Filtering

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

Orbs

Using Orbs

version: 2.1

orbs:
  aws-cli: circleci/[email protected]
  kubernetes: circleci/[email protected]

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

orbs:
  node: circleci/[email protected]              # Node.js
  docker: circleci/[email protected]          # Docker builds
  aws-cli: circleci/[email protected]        # AWS CLI
  aws-ecr: circleci/[email protected]        # ECR push
  aws-ecs: circleci/[email protected]        # ECS deploy
  gcp-cli: circleci/[email protected]        # GCP CLI
  kubernetes: circleci/[email protected]  # K8s deploy
  slack: circleci/[email protected]           # Notifications

Docker Builds

version: 2.1

orbs:
  docker: circleci/[email protected]

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

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

Using Variables

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

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

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

github-actions - GitHub CI/CD
docker-management - Container builds
aws-ecs-fargate - ECS deployments

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take bagelhole/circleci from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Circleci Agent Skill

Install

The instruction itself

CircleCI

When to Use This Skill

Prerequisites

Configuration File

Executors

Docker Executor

Machine Executor

macOS Executor

Caching

Dependency Caching

Multi-Key Caching

Workspaces

Persist Data

Parallelism

Test Splitting

Workflows

Sequential Jobs

Parallel Jobs

Manual Approval

Scheduled Workflows

Branch Filtering

Orbs

Using Orbs

Common Orbs

Docker Builds

Environment Variables

Project Variables

Contexts

Using Variables

Artifacts and Test Results

Resource Classes

Common Issues

Issue: Cache Not Restoring

Issue: Workspace Attach Fails

Issue: Docker Layer Caching

Best Practices

Related Skills

Other skills for the same job

How to use it