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Dotnet Add Ci

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wshaddix
dotnet-add-ci

Adding CI/CD to a .NET project. GitHub Actions vs Azure DevOps detection, workflow templates.

Overview

Publisherwshaddix
Repositorydotnet-skills
Skill namedotnet-add-ci
Stars
79
Forks
13
Bundled files
Instructions only
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 wshaddix on GitHub. Read the source before you install it.

Installation

Install the Dotnet Add Ci 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/wshaddix/dotnet-skills.git /tmp/dotnet-skills
mkdir -p .claude/skills
cp -r /tmp/dotnet-skills/skills/dotnet-add-ci .claude/skills/dotnet-add-ci
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dotnet Add Ci 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 Dotnet Add Ci 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 Dotnet Add Ci 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.

dotnet-add-ci

Add starter CI/CD workflows to an existing .NET project. Detects the hosting platform (GitHub Actions or Azure DevOps) and generates an appropriate starter workflow for build, test, and pack.

Scope boundary: This skill provides starter templates only. For advanced CI/CD patterns — composable reusable workflows, matrix builds, deployment pipelines, release automation, and environment promotion — see [skill:dotnet-gha-patterns], [skill:dotnet-ado-patterns], and related CI/CD depth skills.

Prerequisites: Run [skill:dotnet-version-detection] first to determine SDK version for the workflow. Run [skill:dotnet-project-analysis] to understand solution structure.

Cross-references: [skill:dotnet-project-structure] for build props layout, [skill:dotnet-scaffold-project] which generates the project structure these workflows build.


Platform Detection

Detect the CI platform from existing repo indicators:

IndicatorPlatform
.github/ directory existsGitHub Actions
azure-pipelines.yml existsAzure DevOps
.github/workflows/ has YAML filesGitHub Actions (already configured)
NeitherAsk the user which platform to target

GitHub Actions Starter Workflow

Create .github/workflows/build.yml:

yaml
name: Build and Test

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

permissions:
  contents: read

env:
  DOTNET_NOLOGO: true
  DOTNET_CLI_TELEMETRY_OPTOUT: true
  DOTNET_SKIP_FIRST_TIME_EXPERIENCE: true

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Setup .NET
        uses: actions/setup-dotnet@v4
        with:
          global-json-file: global.json

      - name: Restore
        run: dotnet restore --locked-mode

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

      - name: Test
        run: dotnet test --no-build -c Release --logger trx --results-directory TestResults

      - name: Upload test results
        uses: actions/upload-artifact@v4
        if: always()
        with:
          name: test-results
          path: TestResults/**/*.trx

Key Decisions Explained

  • global-json-file — uses the repo's global.json to install the exact SDK version. If the project has no global.json, replace with dotnet-version: '10.0.x' (or the appropriate version)
  • --locked-mode — ensures packages.lock.json files are respected; fails if they're out of date. If the project doesn't use lock files, replace with plain dotnet restore
  • -c Release — builds in Release mode so ContinuousIntegrationBuild takes effect
  • permissions: contents: read — principle of least privilege
  • Environment variables — suppress .NET CLI noise in logs

Adding NuGet Pack (Libraries)

For projects that publish to NuGet, add a pack step:

yaml
      - name: Pack
        run: dotnet pack --no-build -c Release -o artifacts

      - name: Upload packages
        uses: actions/upload-artifact@v4
        with:
          name: nuget-packages
          path: artifacts/*.nupkg

Azure DevOps Starter Pipeline

Create azure-pipelines.yml at the repo root:

yaml
trigger:
  branches:
    include:
      - main

pr:
  branches:
    include:
      - main

pool:
  vmImage: 'ubuntu-latest'

variables:
  DOTNET_NOLOGO: true
  DOTNET_CLI_TELEMETRY_OPTOUT: true
  DOTNET_SKIP_FIRST_TIME_EXPERIENCE: true
  buildConfiguration: 'Release'

steps:
  - task: UseDotNet@2
    displayName: 'Setup .NET SDK'
    inputs:
      useGlobalJson: true

  - script: dotnet restore --locked-mode
    displayName: 'Restore'

  - script: dotnet build --no-restore -c $(buildConfiguration)
    displayName: 'Build'

  - task: DotNetCoreCLI@2
    displayName: 'Test'
    inputs:
      command: 'test'
      arguments: '--no-build -c $(buildConfiguration) --logger trx'
      publishTestResults: true

Adding NuGet Pack (Libraries)

yaml
  - script: dotnet pack --no-build -c $(buildConfiguration) -o $(Build.ArtifactStagingDirectory)
    displayName: 'Pack'

  - task: PublishBuildArtifacts@1
    displayName: 'Publish NuGet packages'
    inputs:
      pathToPublish: '$(Build.ArtifactStagingDirectory)'
      artifactName: 'nuget-packages'

Adapting the Starter Workflow

Multi-TFM Projects

If the project multi-targets, the default workflow works without changes — dotnet build and dotnet test handle all TFMs automatically. No matrix is needed for the starter.

Windows-Only Projects (MAUI, WPF, WinForms)

Change the runner:

yaml
# GitHub Actions
runs-on: windows-latest

# Azure DevOps
pool:
  vmImage: 'windows-latest'

Solution Filter

If the repo has multiple solutions or uses solution filters:

yaml
      - name: Build
        run: dotnet build MyApp.slnf --no-restore -c Release

Verification

After adding the workflow, verify locally:

bash
# GitHub Actions — validate YAML syntax
# Install: gh extension install moritztomasi/gh-workflow-validator
gh workflow-validator .github/workflows/build.yml

# Or simply verify the build steps work locally
dotnet restore --locked-mode
dotnet build --no-restore -c Release
dotnet test --no-build -c Release

Push a branch and open a PR to trigger the workflow.


What's Next

This starter covers build-test-pack. For advanced scenarios, see the CI/CD depth skills:

  • Reusable composite actions and workflow templates
  • Matrix builds across OS/TFM combinations
  • Deployment pipelines with environment gates
  • NuGet publishing with signing
  • Container image builds
  • Code coverage reporting and enforcement

References

Frequently asked questions

What does the Dotnet Add Ci AI skill do?

Adding CI/CD to a .NET project. GitHub Actions vs Azure DevOps detection, workflow templates.

Why use Dotnet Add Ci on TypingMind?

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

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

Which AI models can use Dotnet Add Ci?

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 Dotnet Add Ci?

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

Is the Dotnet Add Ci AI skill free?

It is published on GitHub by wshaddix. Check the repository for licensing terms. 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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