Memstack Deployment Ci Cd Pipeline logo

Memstack Deployment Ci Cd Pipeline

Community
cwinvestments
memstack-deployment-ci-cd-pipeline

Use this skill when the user says 'CI/CD', 'GitHub Actions', 'pipeline', 'continuous integration', 'continuous deployment', 'ci-cd-pipeline', 'automate deploys', or needs to set up automated build, test, and deployment pipelines. Do NOT use for one-time manual deployments.

Overview

Publishercwinvestments
Repositorymemstack
Skill namememstack-deployment-ci-cd-pipeline
Stars
425
Forks
45
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Memstack Deployment Ci Cd Pipeline 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/cwinvestments/memstack/tree/master/skills/deployment/ci-cd-pipeline
  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/cwinvestments/memstack.git /tmp/memstack
mkdir -p .claude/skills
cp -r /tmp/memstack/skills/deployment/ci-cd-pipeline .claude/skills/memstack-deployment-ci-cd-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memstack Deployment Ci Cd Pipeline 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 Memstack Deployment Ci Cd Pipeline 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 Memstack Deployment Ci Cd Pipeline 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.

🔄 CI/CD Pipeline: Continuous Integration & Deployment

Detect project type and generate a complete CI/CD pipeline with lint, test, build, deploy stages, rollback strategy, and environment management.

Activation

When this skill activates, output:

🔄 CI/CD Pipeline: Designing your pipeline...

ContextStatus
User says "CI/CD", "pipeline", "GitHub Actions"ACTIVE
User wants automated testing and deploymentACTIVE
User mentions branch strategy, deploy gates, or rollbacksACTIVE
User wants Docker setup (CI/CD is secondary)DORMANT: see docker-setup
User wants server provisioning (not pipeline)DORMANT: see hetzner-setup
User wants to deploy to a specific platform onlyDORMANT, see railway-deploy or netlify-deploy

Protocol

Step 1: Gather Inputs

Ask the user for:

  • Project type: Node.js, Python, Go, Rust, monorepo?
  • Testing framework: Jest, Vitest, Pytest, Go test?
  • Deployment target: Vercel, Railway, Netlify, Hetzner VPS, AWS, Docker registry?
  • Current CI/CD: Any existing pipeline, or starting from scratch?
  • Branch strategy: Trunk-based, GitFlow, or custom?
  • Team size: Solo dev, small team, or large team?

Step 2: Detect Project & Recommend Platform

Auto-detect from project files and recommend CI/CD platform:

Project SignalPlatform RecommendationReason
.github/ existsGitHub ActionsAlready on GitHub, native integration
vercel.json or Next.jsVercel (auto-deploy) + GitHub Actions (CI)Vercel handles deploy, GHA handles testing
railway.jsonRailway (auto-deploy) + GitHub Actions (CI)Railway handles deploy, GHA handles testing
netlify.tomlNetlify (auto-deploy) + GitHub Actions (CI)Netlify handles deploy, GHA handles testing
Dockerfile presentGitHub Actions → Docker registry → deployFull control pipeline
Monorepo (packages/)GitHub Actions with matrix/path filtersNeed per-package CI
Self-hosted serverGitHub Actions → SSH deployPush-based deployment

Default recommendation: GitHub Actions: free for public repos, 2,000 min/month for private.

Step 3: Design Pipeline Stages

Define the stage sequence:

┌─────────┐   ┌─────────┐   ┌─────────┐   ┌─────────┐   ┌─────────┐
│  LINT   │──→│  TEST   │──→│  BUILD  │──→│ DEPLOY  │──→│ VERIFY  │
│         │   │         │   │         │   │         │   │         │
│ ESLint  │   │ Unit    │   │ Compile │   │ Push to │   │ Health  │
│ Prettier│   │ Integ.  │   │ Bundle  │   │ target  │   │ Smoke   │
│ Types   │   │ E2E     │   │ Docker  │   │ env     │   │ Rollback│
└─────────┘   └─────────┘   └─────────┘   └─────────┘   └─────────┘
     │              │              │              │              │
  Fail fast    Gate: block     Artifacts      Env-specific    Auto-rollback
  < 1 min      if < 80%       cached         secrets         on failure

Stage details:

StageTriggerFailure ActionDuration Target
LintEvery push, every PRBlock merge< 1 min
TestEvery push, every PRBlock merge< 5 min
BuildPR merge to main/developBlock deploy< 3 min
DeployBuild passes on target branchAlert + rollback< 2 min
VerifyAfter deploy completesAuto-rollback< 1 min

Step 4: Branch Strategy

Recommended: Simplified GitFlow

main (production)
  ├── develop (staging)
  │     ├── feature/add-auth
  │     ├── feature/dashboard
  │     └── fix/login-bug
  └── hotfix/critical-fix (→ main + develop)
BranchDeploys ToCI RunsAuto-Deploy?
feature/*noneLint + TestNo
developStagingLint + Test + Build + DeployYes
mainProductionLint + Test + Build + DeployYes (or manual gate)
hotfix/*noneLint + TestNo (merge to main to deploy)

Branch protection rules:

main:
  - Require PR review (1+ approvals)
  - Require status checks (lint, test, build)
  - No force push
  - No direct push

develop:
  - Require status checks (lint, test)
  - Allow direct push (for solo devs)
  - No force push

Step 5: Environment Variable Management

── ENVIRONMENT VARIABLES ──────────────────

Three environments with escalating secrets:

LOCAL (.env.local, never committed):
  DATABASE_URL=postgresql://localhost:5432/app_dev
  API_KEY=dev_test_key
  NODE_ENV=development

STAGING (GitHub Secrets / platform env):
  DATABASE_URL=[staging DB connection string]
  API_KEY=[staging API key]
  NODE_ENV=staging

PRODUCTION (GitHub Secrets / platform env):
  DATABASE_URL=[production DB connection string]
  API_KEY=[production API key]
  NODE_ENV=production

Secret management rules:

  • Never hardcode secrets in pipeline config
  • Use GitHub Environments for per-branch secrets
  • Use GITHUB_TOKEN for GitHub operations (auto-provided)
  • Rotate secrets quarterly
  • Audit secret access in GitHub Settings → Secrets → Audit log

GitHub Environments setup:

Repository → Settings → Environments:
  staging:
    Secrets: DATABASE_URL, API_KEY
    Deployment branches: develop

  production:
    Secrets: DATABASE_URL, API_KEY
    Deployment branches: main
    Required reviewers: [team member] (optional gate)

Step 6: Generate Pipeline Config

GitHub Actions: Node.js project:

yaml
name: CI/CD Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main, develop]

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

jobs:
  lint:
    name: Lint
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - run: npm ci
      - run: npm run lint
      - run: npm run typecheck  # If TypeScript

  test:
    name: Test
    runs-on: ubuntu-latest
    needs: lint
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - run: npm ci
      - run: npm test -- --coverage
      - uses: actions/upload-artifact@v4
        if: always()
        with:
          name: coverage-report
          path: coverage/

  build:
    name: Build
    runs-on: ubuntu-latest
    needs: test
    if: github.event_name == 'push'
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - run: npm ci
      - run: npm run build
      - uses: actions/upload-artifact@v4
        with:
          name: build-output
          path: dist/

  deploy-staging:
    name: Deploy to Staging
    runs-on: ubuntu-latest
    needs: build
    if: github.ref == 'refs/heads/develop'
    environment: staging
    steps:
      - uses: actions/checkout@v4
      - uses: actions/download-artifact@v4
        with:
          name: build-output
          path: dist/
      # Platform-specific deploy step here
      # Option A: Railway
      # - uses: bervProject/railway-deploy@main
      #   with:
      #     railway_token: ${{ secrets.RAILWAY_TOKEN }}
      # Option B: SSH deploy to VPS
      # - uses: appleboy/ssh-action@v1
      #   with:
      #     host: ${{ secrets.SERVER_HOST }}
      #     username: deploy
      #     key: ${{ secrets.SSH_PRIVATE_KEY }}
      #     port: 2222
      #     script: |
      #       cd /opt/app && git pull && npm ci --production && pm2 restart all

  deploy-production:
    name: Deploy to Production
    runs-on: ubuntu-latest
    needs: build
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: actions/checkout@v4
      - uses: actions/download-artifact@v4
        with:
          name: build-output
          path: dist/
      # Same deploy pattern as staging but with production env

  verify:
    name: Verify Deployment
    runs-on: ubuntu-latest
    needs: [deploy-staging, deploy-production]
    if: always() && (needs.deploy-staging.result == 'success' || needs.deploy-production.result == 'success')
    steps:
      - name: Health check
        run: |
          URL="${{ github.ref == 'refs/heads/main' && secrets.PRODUCTION_URL || secrets.STAGING_URL }}"
          for i in 1 2 3 4 5; do
            STATUS=$(curl -s -o /dev/null -w "%{http_code}" "$URL/health")
            if [ "$STATUS" = "200" ]; then
              echo "Health check passed"
              exit 0
            fi
            echo "Attempt $i: HTTP $STATUS, retrying in 10s..."
            sleep 10
          done
          echo "Health check failed after 5 attempts"
          exit 1

GitHub Actions: Python project:

yaml
name: CI/CD Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main, develop]

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.12'
          cache: 'pip'
      - run: pip install ruff mypy
      - run: ruff check .
      - run: mypy .

  test:
    runs-on: ubuntu-latest
    needs: lint
    services:
      postgres:
        image: postgres:16
        env:
          POSTGRES_USER: test
          POSTGRES_PASSWORD: test
          POSTGRES_DB: testdb
        ports: ['5432:5432']
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.12'
          cache: 'pip'
      - run: pip install -r requirements.txt
      - run: pytest --cov --cov-report=xml
        env:
          DATABASE_URL: postgresql://test:test@localhost:5432/testdb

  build-and-deploy:
    runs-on: ubuntu-latest
    needs: test
    if: github.event_name == 'push'
    steps:
      - uses: actions/checkout@v4
      # Docker build + push or direct deploy

Step 7: Deployment Rollback Strategy

Automated rollback on failed health check:

yaml
  rollback:
    name: Rollback on Failure
    runs-on: ubuntu-latest
    needs: verify
    if: failure()
    steps:
      - name: Rollback deployment
        run: |
          echo "Deployment verification failed: rolling back"
          # Option A: Revert to previous Docker image
          # docker pull $REGISTRY/app:previous && docker tag $REGISTRY/app:previous $REGISTRY/app:latest
          # Option B: Railway rollback
          # railway rollback
          # Option C: Git revert + redeploy
          # git revert HEAD --no-edit && git push
      - name: Notify team
        run: |
          curl -X POST "${{ secrets.SLACK_WEBHOOK }}" \
            -H 'Content-Type: application/json' \
            -d '{"text":"🔴 Deployment rolled back, health check failed on ${{ github.ref }}"}'

Rollback strategies by deployment target:

TargetRollback MethodSpeedData Safety
VercelInstant rollback in dashboard or CLIInstantSafe: immutable deploys
Railwayrailway rollback or dashboardInstantSafe: previous deploy preserved
NetlifyDeploy previous build in dashboardInstantSafe: immutable deploys
DockerTag previous image as latest, restartSecondsSafe: images preserved
VPS/PM2git revert + pm2 restartMinutesCheck DB migrations first
Kuberneteskubectl rollout undoSecondsCheck DB migrations first

Database migration caution:

  • If the deploy includes DB migrations, rollback is complex
  • Always write reversible migrations (up + down)
  • Test down migration before deploying up
  • Consider: deploy DB migration separately from code deploy

Step 8: Notifications

Slack notification on deploy:

yaml
  notify:
    name: Notify
    runs-on: ubuntu-latest
    needs: [deploy-staging, deploy-production, verify]
    if: always()
    steps:
      - name: Send Slack notification
        env:
          SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
        run: |
          if [ "${{ needs.verify.result }}" = "success" ]; then
            EMOJI="✅"
            STATUS="succeeded"
          else
            EMOJI="🔴"
            STATUS="failed"
          fi
          curl -X POST "$SLACK_WEBHOOK" \
            -H 'Content-Type: application/json' \
            -d "{
              \"text\": \"${EMOJI} Deploy ${STATUS}\",
              \"blocks\": [
                {
                  \"type\": \"section\",
                  \"text\": {
                    \"type\": \"mrkdwn\",
                    \"text\": \"${EMOJI} *Deploy ${STATUS}*\nBranch: \`${{ github.ref_name }}\`\nCommit: \`${{ github.sha }}\`\nBy: ${{ github.actor }}\"
                  }
                }
              ]
            }"

Alternative notification channels:

  • GitHub Actions built-in: Email on failure (Settings → Notifications)
  • Discord webhook: Same curl pattern, different payload format
  • PagerDuty: For production failures that need on-call response

Step 9: Output

Present the complete CI/CD configuration:

━━━ CI/CD PIPELINE ━━━━━━━━━━━━━━━━━━━━━━━
Project: [name]
Platform: GitHub Actions
Deploy target: [platform]
Branch strategy: [strategy]

── PIPELINE STAGES ────────────────────────
Lint → Test → Build → Deploy → Verify
[stage diagram with timing]

── BRANCH STRATEGY ────────────────────────
[branch → environment mapping]
[protection rules]

── ENVIRONMENT VARIABLES ──────────────────
[per-environment secret setup]

── PIPELINE CONFIG ────────────────────────
[complete .github/workflows/ci-cd.yml]

── ROLLBACK STRATEGY ──────────────────────
[per-target rollback method]
[database migration caution]

── NOTIFICATIONS ──────────────────────────
[Slack/Discord/email setup]

── SETUP CHECKLIST ────────────────────────
[ ] Create .github/workflows/ directory
[ ] Add pipeline YAML file
[ ] Configure GitHub Environments (staging, production)
[ ] Add secrets to each environment
[ ] Set branch protection rules
[ ] Configure notification webhook
[ ] Test pipeline with a feature branch PR
[ ] Verify staging deploy on develop merge
[ ] Verify production deploy on main merge

Inputs

  • Project type and language
  • Testing framework
  • Deployment target
  • Branch strategy preference
  • Team size
  • Existing CI/CD (if any)

Outputs

  • Pipeline stage design with timing targets
  • Branch strategy with protection rules
  • Environment variable management per stage
  • Complete CI/CD config file (GitHub Actions YAML)
  • Deployment rollback strategy per target
  • Notification setup (Slack, Discord, email)
  • Setup checklist for first-time configuration

Level History

  • Lv.1: Base: Auto-detect project type, 5-stage pipeline (lint/test/build/deploy/verify), GitHub Actions configs for Node.js and Python, simplified GitFlow branch strategy, environment variable management with GitHub Environments, automated rollback on health check failure, Slack notifications, setup checklist. (Origin: MemStack v3.2, Mar 2026)

Frequently asked questions

What does the Memstack Deployment Ci Cd Pipeline AI skill do?

Use this skill when the user says 'CI/CD', 'GitHub Actions', 'pipeline', 'continuous integration', 'continuous deployment', 'ci-cd-pipeline', 'automate deploys', or needs to set up automated build, test, and deployment pipelines. Do NOT use for one-time manual deployments.

Why use Memstack Deployment Ci Cd Pipeline on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cwinvestments/memstack/tree/master/skills/deployment/ci-cd-pipeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memstack Deployment Ci Cd Pipeline?

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 Memstack Deployment Ci Cd Pipeline?

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

Is the Memstack Deployment Ci Cd Pipeline AI skill free?

Yes. It is published on GitHub by cwinvestments 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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