Build, test, and deploy applications using GitHub Actions workflows. Create CI/CD pipelines, configure runners, manage secrets, and automate software delivery. Use when working with GitHub repositories, automating builds, running tests, or deploying applications.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill github-actions
Automate software workflows directly in your GitHub repository with GitHub Actions.
Use this skill when:
Workflows are defined in .github/workflows/ directory:
name: CI Pipeline
on:
push:
branches: [main, develop]
pull_request:
branches: [main]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: '20'
cache: 'npm'
- run: npm ci
- run: npm test
on:
push:
branches: [main]
paths:
- 'src/**'
- 'package.json'
pull_request:
branches: [main]
on:
schedule:
- cron: '0 2 * * *' # Daily at 2 AM UTC
on:
workflow_dispatch:
inputs:
environment:
description: 'Deployment environment'
required: true
default: 'staging'
type: choice
options:
- staging
- production
jobs:
test:
runs-on: ubuntu-latest
strategy:
matrix:
node-version: [18, 20, 22]
os: [ubuntu-latest, windows-latest]
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: ${{ matrix.node-version }}
- run: npm test
jobs:
build:
runs-on: ubuntu-latest
steps:
- run: npm run build
test:
needs: build
runs-on: ubuntu-latest
steps:
- run: npm test
deploy:
needs: [build, test]
runs-on: ubuntu-latest
steps:
- run: ./deploy.sh
jobs:
deploy:
runs-on: ubuntu-latest
environment:
name: production
url: https://example.com
steps:
- run: ./deploy.sh
steps:
- name: Deploy
env:
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
run: aws s3 sync ./dist s3://my-bucket
steps:
- name: Build
env:
API_URL: ${{ vars.API_URL }}
run: npm run build
- uses: actions/cache@v4
with:
path: ~/.npm
key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}
restore-keys: |
${{ runner.os }}-node-
- uses: actions/upload-artifact@v4
with:
name: build-output
path: dist/
retention-days: 5
- uses: actions/download-artifact@v4
with:
name: build-output
path: dist/
jobs:
docker:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
- name: Build and push
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: user/app:latest
# .github/workflows/reusable-deploy.yml
name: Reusable Deploy
on:
workflow_call:
inputs:
environment:
required: true
type: string
secrets:
deploy_key:
required: true
jobs:
deploy:
runs-on: ubuntu-latest
environment: ${{ inputs.environment }}
steps:
- run: echo "Deploying to ${{ inputs.environment }}"
jobs:
deploy-staging:
uses: ./.github/workflows/reusable-deploy.yml
with:
environment: staging
secrets:
deploy_key: ${{ secrets.STAGING_KEY }}
# Download runner
mkdir actions-runner && cd actions-runner
curl -o actions-runner-linux-x64.tar.gz -L https://github.com/actions/runner/releases/download/v2.311.0/actions-runner-linux-x64-2.311.0.tar.gz
tar xzf actions-runner-linux-x64.tar.gz
# Configure
./config.sh --url https://github.com/OWNER/REPO --token TOKEN
# Run
./run.sh
jobs:
build:
runs-on: self-hosted
steps:
- uses: actions/checkout@v4
Set repository secrets:
ACTIONS_RUNNER_DEBUG: trueACTIONS_STEP_DEBUG: true- name: Debug
run: |
echo "GitHub context: ${{ toJson(github) }}"
echo "Job context: ${{ toJson(job) }}"
Problem: Workflow doesn't run on push/PR
Solution: Check branch filters, path filters, and ensure workflow file is on the default branch
Problem: Actions can't push or create PRs
Solution: Configure permissions in workflow or update repository settings
permissions:
contents: write
pull-requests: write
Problem: Cache misses despite existing cache
Solution: Verify cache key matches exactly, check runner OS
continue-on-errorAssess 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.
Take bagelhole/github-actions from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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same name cannot sit side by side — one of them will be ignored.