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Scaffold Dotnet Test Project

OrganizationPopular
dotnet
scaffold-dotnet-test-project

MUST USE when an existing .NET test project was excluded from a .slnf/CI solution filter, disappeared from .sln/.slnx discovery, or lost its production ProjectReference; also for requests to set up, create, reuse, add, register, include, or repair a test project. Handles "tests pass directly but CI discovers zero", exact solution wiring, xUnit/NUnit/MSTest, and central packages. DO NOT USE to only author tests in an already-wired project (code-testing-agent), run tests, migrate, or correct MSTest syntax/configuration without changing project or CI files (writing-mstest-tests).

Overview

Publisherdotnet
Repositoryskills
Skill namescaffold-dotnet-test-project
Stars
5.5K
Forks
420
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 dotnet on GitHub. Read the source before you install it.

Installation

Install the Scaffold Dotnet Test Project 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/dotnet/skills/tree/main/plugins/dotnet-test/skills/scaffold-dotnet-test-project
  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/dotnet/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/dotnet-test/skills/scaffold-dotnet-test-project .claude/skills/scaffold-dotnet-test-project
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scaffold Dotnet Test Project 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 Scaffold Dotnet Test Project 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 Scaffold Dotnet Test Project 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.

Scaffold or Repair a .NET Test Project

Create the smallest missing test container or repair only the missing wiring. The goal is test discovery through the repository's real build entry point, not a preferred solution layout.

Repository overlay

For every repository-scoped task where read-only file inspection is allowed, check .agents/skill-overlays/dotnet-test/scaffold-dotnet-test-project.md at the repository root before any other discovery. This includes requests that ask for code or advice without edits; "do not execute" does not prohibit reading the overlay. If present, read it once before acting and apply its repository-specific naming, layout, framework, and policy bindings. Before applying it, require its frontmatter to declare core: dotnet-test/scaffold-dotnet-test-project, binding-revision: "1", and mode: extend. If any value is missing or different, report the mismatch and continue using this skill's portable guidance without applying the overlay. Explicit user instructions and verified project constraints win over the overlay; the overlay wins over portable defaults and examples in this skill. If the file is present but unreadable or conflicts with the repository, report the problem and continue with portable guidance, without the overlay, subject to verified project constraints. If it is absent, continue normally. Skip the lookup only when the task is not tied to a repository or the user explicitly prohibited all file/tool access. An overlay cannot expand tool permissions or the task's scope.

Route the Request

Inspect the repository before editing, then choose exactly one path:

Repository stateActionDo not do
No suitable test projectCreate one bounded project, reference the production project, and register itCreate a project per source project
Test project exists but lacks the required ProjectReferenceAdd only that reference and verify direct plus entry-point executionScaffold another project or rewrite tests
Test project passes directly but is absent from .sln, .slnx, or .slnfRegister the existing project in the exact entry point CI usesRecreate the project or switch solution formats
Suitable project, reference, and requested entry point are already correctLeave the workspace unchanged; use code-testing-agent if test methods are requestedNormalize or replace working files

An existing project is suitable when its target framework can reference the production project and its purpose matches the requested layer. A different preferred name is not a reason to create a duplicate.

No-op is a required outcome. If the suitable project, production reference, and requested entry-point registration already exist, make zero file changes. Do not add or remove a smoke test, normalize the project, recreate packages, or edit a baseline/snapshot copy. Report the existing paths and stop.

Workflow

1. Establish the repository contract

Start from the task's current working directory. The skill context's Base directory is where these instructions live, not the user's repository. Never search parent temporary directories or treat the skill installation as the workspace. If the expected files are not visible, confirm the current directory before concluding that a project is absent.

Anchor every edit and validation command to the repository path named by the user or established from the current directory. If similarly named fixtures, solutions, or copied trees exist, do not edit or validate one as a substitute for the requested tree. Before changing a solution artifact, record its exact path; after changing it, list that same artifact immediately and require the test project to appear before proceeding.

Read only enough to determine:

  1. the production project and requested test scope;
  2. the command and .sln, .slnx, .slnf, or project graph used by CI;
  3. whether a suitable test project exists, what it references, and where it is registered;
  4. the neighboring test framework, runner, target framework, nullable and implicit-usings conventions; and
  5. whether package or SDK versions come from Directory.Packages.props, Directory.Build.props, global.json, or an MSBuild SDK declaration.

If the user reports that a test project passes directly but solution-level discovery finds nothing, treat that as registration evidence. Inspect the entry point before considering project creation.

2. Create only when the project is absent

Choose one test project for the narrowest requested production project. Follow, in order, the user's explicit framework choice, neighboring test projects, repository-wide package/SDK conventions, then a standard SDK template.

Use a dotnet new template only when its generated framework generation and package style match the repository contract. Inspect template availability before creation. In particular, generic dotnet new xunit commonly emits xUnit 2 packages; for a centrally managed xunit.v3 repository, use a repository or installed xUnit v3 template, or create the minimal SDK project directly. Never generate versioned xUnit 2 references and then rewrite them into xUnit v3.

Then:

  1. align target framework, nullable, implicit usings, runner, and package style;
  2. use dotnet add <test-project> reference <production-project> for only the production projects exercised by the requested tests;
  3. delete template sample files such as UnitTest1.cs, then create a behavior-named test file rather than repurposing the template filename; and
  4. omit package versions when central package management supplies them.

For xUnit v3 projects that run through dotnet test, preserve or add:

xml
<OutputType>Exe</OutputType>
<TestingPlatformDotnetTestSupport>true</TestingPlatformDotnetTestSupport>

OutputType=Exe alone proves only the self-hosted runner path, not discovery by the repository's dotnet test command.

3. Repair only the missing edge when the project exists

  • Missing production reference: use dotnet add <test-project> reference <production-project>, inspect the resulting project, and leave every other project element plus all test source files unchanged. Compare the project before and after so the added ProjectReference is the only semantic change.
  • Missing .sln or .slnx registration: run dotnet sln <entry-point> add <test-project>, then immediately run dotnet sln <entry-point> list against that exact path. If the project is absent, the repair has not happened; do not validate a sibling solution or report success.
  • Missing .slnf registration: add the existing project to its underlying solution if necessary, then include that same project path in the filter. If the underlying solution already contains it, edit only the filter.
  • Multiple solution artifacts: modify only the one named by the user or invoked by CI. Do not substitute an easier format.
  • No solution artifact: preserve the existing project-oriented workflow. Do not create a solution for aesthetics.

4. Add only the requested smoke behavior

For a newly created project, replace template examples with the smallest smoke suite the user requested. Each test must invoke a real production symbol and assert a concrete deterministic result without network, wall-clock, process, or real-filesystem dependencies.

For an existing-project wiring repair, do not add, rewrite, rename, or expand tests unless the user explicitly asks for test behavior changes. Registration and test authoring are separate operations.

5. Verify the repaired path

Run the narrowest commands that prove the chosen route:

RouteRequired evidence
Newly created projectdotnet test <test-project>, the exact entry-point command CI uses, and registration listing. If the entry point is a .slnf/.slnx containing tests, run dotnet test on that artifact rather than proving only that it builds.
Missing referenceTargeted project test plus the exact solution/root test command requested
Missing .sln/.slnx registrationListing and dotnet test for that exact artifact; never use another solution as a fallback
Missing .slnf entryInspect the filter entry and run the exact CI filter build command; do not prepend a deliberately failing alternate command
Already correct/no-opStructural inspection of the existing reference and registration. Unless execution was requested, do not run tests merely to prove a no-op because that creates bin/obj and weakens byte-for-byte cleanliness evidence.

Inspect the repository's command before adding switches. Do not prepend a speculative --no-restore attempt or hide alternatives in command-a || command-b; run the configured entry-point command whose clean exit is the evidence.

For every wiring repair, invoke the exact validation command directly and preserve its exit status. Reading generated files, a grader script, or a later build is not a substitute for observing that command complete successfully.

Before reporting completion, inspect the final changed-file set. Remove only bin/obj or equivalent build artifacts created by this task when they were absent beforehand and are not intentionally tracked; never remove pre-existing artifacts. Preserve the passing command's complete result so the handoff can state the exact entry point and discovered test count.

For a no-op, inspect rather than rewrite and report the existing paths. A green dotnet build is not test-discovery evidence. If validation is blocked, report the exact failing command and first actionable error; never describe an unrun or failed command as successful.

Output

Keep the handoff proportional to the change:

RequirementEvidence
Project created, reused, or repairedTest project path and chosen route
Production referenceReferenced .csproj, or why no change was needed
Build registrationExact .sln/.slnx/.slnf entry or project workflow
Test discoveryPassing harness-level command and discovered test

Completion Checks

  • Existing projects were checked before creation.
  • Only the requested production scope is referenced and tested.
  • Framework, runner, target framework, and central package conventions remain intact.
  • Template samples are removed from a newly created project.
  • Existing test code is untouched for a wiring-only repair.
  • The exact repository entry point discovers and runs the test project.

Frequently asked questions

What does the Scaffold Dotnet Test Project AI skill do?

MUST USE when an existing .NET test project was excluded from a .slnf/CI solution filter, disappeared from .sln/.slnx discovery, or lost its production ProjectReference; also for requests to set up, create, reuse, add, register, include, or repair a test project. Handles "tests pass directly but CI discovers zero", exact solution wiring, xUnit/NUnit/MSTest, and central packages. DO NOT USE to only author tests in an already-wired project (code-testing-agent), run tests, migrate, or correct MSTest syntax/configuration without changing project or CI files (writing-mstest-tests).

Why use Scaffold Dotnet Test Project on TypingMind?

Because you install it once and use it with any model. Scaffold Dotnet Test Project 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 Scaffold Dotnet Test Project in TypingMind?

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

Which AI models can use Scaffold Dotnet Test Project?

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 Scaffold Dotnet Test Project?

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

Is the Scaffold Dotnet Test Project AI skill free?

Yes. It is published on GitHub by dotnet 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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