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Cd Pipeline Generator

Community
ArabelaTso
cd-pipeline-generator

Generate GitHub Actions deployment workflows for automated deployment to staging and production environments on cloud platforms (AWS, GCP, Azure). Use when setting up continuous deployment pipelines, creating deployment automation, or configuring multi-environment deployment strategies. Includes templates for environment-specific deployments with approval gates, secrets management, and rollback capabilities.

Overview

PublisherArabelaTso
RepositorySkills-4-SE
Skill namecd-pipeline-generator
Stars
258
Forks
25
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Cd Pipeline Generator 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/ArabelaTso/Skills-4-SE/tree/main/skills/cd-pipeline-generator
  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/ArabelaTso/Skills-4-SE.git /tmp/Skills-4-SE
mkdir -p .claude/skills
cp -r /tmp/Skills-4-SE/skills/cd-pipeline-generator .claude/skills/cd-pipeline-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cd Pipeline Generator 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 Cd Pipeline Generator 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 Cd Pipeline Generator 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.

CD Pipeline Generator

Overview

Generate production-ready GitHub Actions deployment workflows that automate deployments to staging and production environments with environment protection rules, approval gates, and secrets management.

Workflow

1. Identify Deployment Target

Determine the cloud platform and deployment method:

  • AWS: ECS, Elastic Beanstalk, EC2, Lambda
  • GCP: Cloud Run, App Engine, Compute Engine, Cloud Functions
  • Azure: App Service, Container Instances, Functions

2. Select Template

Use the appropriate template from assets/ based on cloud platform:

  • deploy-aws.yml - AWS deployments (ECS, Elastic Beanstalk, Lambda)
  • deploy-gcp.yml - GCP deployments (Cloud Run, App Engine)
  • deploy-azure.yml - Azure deployments (App Service, Container Instances)

3. Configure Environments

Set up GitHub environment protection rules for staging and production:

Staging environment:

  • Auto-deploy on merge to main/master
  • No approval required
  • Use staging secrets and variables

Production environment:

  • Manual approval required
  • Deploy on workflow_dispatch or tag push
  • Use production secrets and variables
  • Optional: Restrict to specific branches

4. Configure Secrets

Add required secrets to GitHub repository settings (Settings → Secrets and variables → Actions):

AWS:

  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY
  • AWS_REGION

GCP:

  • GCP_PROJECT_ID
  • GCP_SERVICE_ACCOUNT_KEY

Azure:

  • AZURE_CREDENTIALS
  • AZURE_SUBSCRIPTION_ID

5. Customize Deployment Steps

Adapt the template to project-specific deployment needs:

Build artifacts: Add build steps before deployment

yaml
- name: Build application
  run: npm run build  # or: python -m build, go build, cargo build

Docker images: Build and push container images

yaml
- name: Build Docker image
  run: docker build -t $IMAGE_NAME:$TAG .

- name: Push to registry
  run: docker push $IMAGE_NAME:$TAG

Database migrations: Run migrations before deployment

yaml
- name: Run migrations
  run: npm run migrate  # or: alembic upgrade head, rails db:migrate

Health checks: Verify deployment success

yaml
- name: Health check
  run: curl -f https://$DEPLOYMENT_URL/health || exit 1

6. Set Deployment Triggers

Configure when deployments run:

Staging: Auto-deploy on push to main

yaml
on:
  push:
    branches: [main]

Production: Manual trigger or tag-based

yaml
on:
  workflow_dispatch:
  push:
    tags:
      - 'v*'

7. Place Workflow File

Create deployment workflow at .github/workflows/deploy.yml. If multiple deployment workflows are needed (e.g., separate staging and production), use descriptive names:

  • .github/workflows/deploy-staging.yml
  • .github/workflows/deploy-production.yml

Template Features

All templates include:

  • Environment separation: Distinct staging and production deployments
  • Approval gates: Production deployments require manual approval
  • Secrets management: Secure credential handling via GitHub secrets
  • Deployment status: Clear success/failure reporting
  • Rollback support: Easy revert to previous versions
  • Conditional execution: Deploy only when tests pass

Security Best Practices

  • Never commit credentials or API keys to the repository
  • Use GitHub environments to scope secrets appropriately
  • Enable required reviewers for production deployments
  • Use OIDC authentication instead of long-lived credentials when possible
  • Implement deployment windows for production (e.g., business hours only)
  • Add deployment notifications to Slack/email

Customization Examples

Add deployment notification:

yaml
- name: Notify deployment
  if: always()
  uses: 8398a7/action-slack@v3
  with:
    status: ${{ job.status }}
    text: 'Deployment to ${{ github.event.inputs.environment }} ${{ job.status }}'
  env:
    SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK }}

Add rollback capability:

yaml
- name: Rollback on failure
  if: failure()
  run: |
    echo "Deployment failed, rolling back..."
    # Platform-specific rollback commands

Restrict production deployment time:

yaml
- name: Check deployment window
  run: |
    HOUR=$(date +%H)
    if [ $HOUR -lt 9 ] || [ $HOUR -gt 17 ]; then
      echo "Deployments only allowed 9 AM - 5 PM"
      exit 1
    fi

Tips

  • Start with staging deployments to validate the workflow
  • Use environment-specific configuration files (e.g., config.staging.json, config.production.json)
  • Implement blue-green or canary deployments for zero-downtime updates
  • Add deployment metrics and monitoring
  • Document rollback procedures in the repository
  • Test deployment workflows in a separate test environment first

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 Cd Pipeline Generator AI skill do?

Generate GitHub Actions deployment workflows for automated deployment to staging and production environments on cloud platforms (AWS, GCP, Azure). Use when setting up continuous deployment pipelines, creating deployment automation, or configuring multi-environment deployment strategies. Includes templates for environment-specific deployments with approval gates, secrets management, and rollback capabilities.

Why use Cd Pipeline Generator on TypingMind?

Because you install it once and use it with any model. Cd Pipeline Generator 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 Cd Pipeline Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/cd-pipeline-generator. 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 Cd Pipeline Generator?

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 Cd Pipeline Generator?

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

Is the Cd Pipeline Generator AI skill free?

Yes. It is published on GitHub by ArabelaTso under the Apache-2.0 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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