Ci Cd logo

Ci Cd

Organization
codewithmukesh
ci-cd

CI/CD pipelines for .NET applications. Covers GitHub Actions and Azure DevOps YAML pipelines with build, test, publish, and deploy stages. Load this skill when setting up continuous integration, automated testing, deployment workflows, or when the user mentions "CI/CD", "pipeline", "GitHub Actions", "Azure DevOps", "workflow", "deploy", "build pipeline", "publish", "NuGet push", "release", or "continuous integration".

Overview

Publishercodewithmukesh
Repositorydotnet-claude-kit
Skill nameci-cd
Stars
721
Forks
170
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 codewithmukesh on GitHub. Read the source before you install it.

Installation

Install the Ci Cd 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.
  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/codewithmukesh/dotnet-claude-kit.git /tmp/dotnet-claude-kit
mkdir -p .claude/skills
cp -r /tmp/dotnet-claude-kit/skills/ci-cd .claude/skills/ci-cd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ci Cd 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 Ci Cd 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 Ci Cd 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

Core Principles

  1. Pipeline as code — YAML pipelines committed to the repo. No click-ops in the UI.
  2. Fast feedback — Build and test on every push. Cache NuGet packages. Fail fast.
  3. Build once, deploy many — Build the artifact once, promote it through environments (dev → staging → production).
  4. Never skip tests — Tests gate the pipeline. No deployment without passing tests.

Patterns

GitHub Actions — Build + Test

yaml
# .github/workflows/ci.yml
name: CI

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

env:
  DOTNET_VERSION: '10.0.x'
  DOTNET_NOLOGO: true
  DOTNET_CLI_TELEMETRY_OPTOUT: true

jobs:
  build-and-test:
    runs-on: ubuntu-latest

    services:
      postgres:
        image: postgres:18
        env:
          POSTGRES_DB: testdb
          POSTGRES_USER: postgres
          POSTGRES_PASSWORD: postgres
        ports:
          - 5432:5432
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5

    steps:
      - uses: actions/checkout@v5

      - name: Setup .NET
        uses: actions/setup-dotnet@v5
        with:
          dotnet-version: ${{ env.DOTNET_VERSION }}

      - name: Restore
        run: dotnet restore

      - name: Build
        run: dotnet build --no-restore --configuration Release

      - name: Format check
        run: dotnet format --verify-no-changes --no-restore

      - name: Test
        run: dotnet test --no-build --configuration Release --logger trx --results-directory TestResults
        env:
          ConnectionStrings__Default: "Host=localhost;Database=testdb;Username=postgres;Password=postgres"

      - name: Publish test results
        uses: actions/upload-artifact@v5
        if: always()
        with:
          name: test-results
          path: TestResults/*.trx

GitHub Actions — Build + Publish Docker Image

yaml
# .github/workflows/publish.yml
name: Publish

on:
  push:
    tags: ['v*']

jobs:
  publish:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write

    steps:
      - uses: actions/checkout@v5

      - name: Login to GitHub Container Registry
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Extract version from tag
        id: version
        run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT

      - name: Build and push
        uses: docker/build-push-action@v6
        with:
          context: .
          push: true
          tags: |
            ghcr.io/${{ github.repository }}:${{ steps.version.outputs.VERSION }}
            ghcr.io/${{ github.repository }}:latest

Azure DevOps — Build + Test

Same restore → build → format → test flow as GitHub Actions. Key differences:

yaml
# azure-pipelines.yml
trigger:
  branches:
    include: [main]
  paths:
    exclude: ['*.md', docs/]

pool:
  vmImage: 'ubuntu-latest'          # vs runs-on: ubuntu-latest

variables:
  dotnetVersion: '10.0.x'

# Key task differences from GitHub Actions:
#   Setup .NET:  task: UseDotNet@2  (inputs: version: $(dotnetVersion))
#   Test results: task: PublishTestResults@2  (testResultsFormat: VSTest)
#   Steps use `script:` + `displayName:` instead of `- name:` + `run:`
#   Services (e.g., Postgres) require a separate Docker task or pipeline service connection

NuGet Package Publishing

yaml
# Part of GitHub Actions workflow
- name: Pack
  run: dotnet pack src/MyLibrary -c Release -o ./nupkg --no-build

- name: Push to NuGet
  run: dotnet nuget push ./nupkg/*.nupkg --api-key ${{ secrets.NUGET_API_KEY }} --source https://api.nuget.org/v3/index.json

Anti-patterns

Don't Build Different Artifacts per Environment

yaml
# BAD — building separately for each environment
- script: dotnet publish -c Debug   # for dev
- script: dotnet publish -c Release # for prod

# GOOD — build once, deploy everywhere
- script: dotnet publish -c Release -o ./publish
# Then deploy the same ./publish artifact to dev, staging, prod

Don't Skip Format Checks in CI

yaml
# BAD — no format enforcement
steps:
  - run: dotnet build
  - run: dotnet test

# GOOD — format check catches style issues early
steps:
  - run: dotnet build
  - run: dotnet format --verify-no-changes
  - run: dotnet test

Don't Hardcode Secrets in Pipelines

yaml
# BAD — secret in pipeline YAML
env:
  DB_PASSWORD: "my-secret-password"

# GOOD — use pipeline secrets
env:
  DB_PASSWORD: ${{ secrets.DB_PASSWORD }}

Decision Guide

ScenarioRecommendation
Open source projectGitHub Actions
Enterprise with AzureAzure DevOps Pipelines
Docker deploymentMulti-stage build in CI, push to container registry
NuGet libraryBuild → Test → Pack → Push on tag
Database migrationsRun in CI test stage, script for production
Environment promotionSame artifact, different configuration

Frequently asked questions

What does the Ci Cd AI skill do?

CI/CD pipelines for .NET applications. Covers GitHub Actions and Azure DevOps YAML pipelines with build, test, publish, and deploy stages. Load this skill when setting up continuous integration, automated testing, deployment workflows, or when the user mentions "CI/CD", "pipeline", "GitHub Actions", "Azure DevOps", "workflow", "deploy", "build pipeline", "publish", "NuGet push", "release", or "continuous integration".

Why use Ci Cd on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codewithmukesh/dotnet-claude-kit/tree/main/skills/ci-cd. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ci Cd?

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 Ci Cd?

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

Is the Ci Cd AI skill free?

Yes. It is published on GitHub by codewithmukesh 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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