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Scaffold

Organization
codewithmukesh
scaffold

Architecture-aware feature scaffolding for .NET 10 projects. Detects the project's architecture (VSA, Clean Architecture, DDD, Modular Monolith) and generates complete feature slices with all required layers: endpoint, handler, validator, DTOs, EF configuration, and integration tests — with the completeness checklist and per-architecture code templates every generated feature must satisfy. Use when: "scaffold", "create feature", "add feature", "new endpoint", "generate", "add entity", "scaffold a module", "add module", or when customizing generation templates or defining what a complete feature slice includes.

Overview

Publishercodewithmukesh
Repositorydotnet-claude-kit
Skill namescaffold
Stars
721
Forks
170
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Scaffold 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/scaffold .claude/skills/scaffold
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scaffold 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 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 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 — Architecture-Aware Feature Scaffolding

What

Generates a complete feature with all required files based on the project's architecture. Never generates half a feature — every scaffold includes the endpoint, handler, validation, DTOs, EF configuration, and at least one integration test as a single unit, written in modern C# 14 (primary constructors, collection expressions, records, sealed handlers, TypedResults).

Supported architectures (file placement maps and code shape templates live in references/architecture-patterns.md):

  • Vertical Slice Architecture (VSA) — single-file features in Features/
  • Clean Architecture (CA) — files split across Domain, Application, Infrastructure, Api
  • DDD + Clean Architecture — aggregate roots, value objects, domain events, plus CA layers
  • Modular Monolith — self-contained modules with their own DbContext and integration events

When

  • "Scaffold a [feature name]", "create an endpoint for", "add a feature"
  • "Generate CRUD for", "add entity", "new module", "scaffold a module"
  • Starting a new feature after /plan has produced an approved plan
  • Customizing generation templates or defining what a complete slice includes
  • Any time the user wants a complete, working feature skeleton

How

Step 1: Detect Architecture

Use the architecture-advisor skill to determine the project's architecture:

  • Examine folder structure, project references, and existing patterns
  • If architecture is ambiguous, ask the user rather than guessing
  • Load the matching architecture skill (vertical-slice, clean-architecture, ddd)

Step 2: Clarify Scope

Confirm with the user before generating (skip anything the plan already answers):

  1. Feature/entity name and operations needed — full CRUD or a subset?
  2. Key fields and invariants for any new entity
  3. Module placement (Modular Monolith only) — existing module or new one?

Step 3: Learn Conventions

Use the convention-learner skill and MCP tools to check:

  • Naming patterns (*Handler, *Service, *Endpoint, *Command, *Query)
  • Folder structure, file organization, access modifiers, sealed conventions
  • Existing validation approach (FluentValidation, data annotations, manual)
  • Test project structure and naming (*Tests, *IntegrationTests)

Match what exists. Do not impose new conventions on an established codebase.

Step 4: Generate All Layers

Generate every file the architecture requires, following the templates in references/architecture-patterns.md:

  • VSA — read the VSA section: single-file feature + endpoint group + EF config + tests
  • Clean Architecture — read the CA section: Mediator command/handler in Application, endpoint in Api
  • DDD — read the DDD section: aggregate with invariants and domain events, thin handler
  • Modular Monolith — read the Modular Monolith section: module DbContext, DI registration, integration events

The reference also covers the shared shapes every architecture reuses (endpoint group, validator, entity + IEntityTypeConfiguration<T> pair, test fixture) and the anti-patterns to avoid.

Step 5: Completeness Checklist (MANDATORY)

Every scaffolded feature MUST include ALL nine items. Do not skip any:

  • EndpointIEndpointGroup file with a route group; never wired in Program.cs
  • Handlersealed, primary constructor, one per operation
  • Validator — FluentValidation rules with meaning (ranges, required, max lengths), wired via .AddEndpointFilter<ValidationFilter<T>>() on mutating endpoints
  • DTOs — records shaped for the consumer, never 1:1 entity mirrors
  • EF configurationIEntityTypeConfiguration<T>; no data annotations on entities
  • Integration testsWebApplicationFactory + Testcontainers, DI replacement via services.RemoveAll<DbContextOptions<T>>()
  • OpenAPI metadata.WithName(), .WithSummary(), .Produces<T>(), .ProducesValidationProblem(), .ProducesProblem(404)
  • CancellationToken — on every async method and passed to every async call
  • Result pattern — handlers return Result<T>; endpoints map success → TypedResults, failure → ToProblemDetails()

Also verify supporting infrastructure — scaffold it if missing: list endpoints get bounded pagination (page/pageSize, max 50), Program.cs has app.UseExceptionHandler(), and appsettings.json has a connection string.

Step 6: Verify

Prove the scaffold works before reporting done:

bash
dotnet build --no-restore
dotnet test --no-build --filter "FullyQualifiedName~{FeatureName}"

If the build or tests fail, fix and re-run before presenting results.

Example

User: /scaffold a Product Catalog feature with CRUD operations

Claude: Detected architecture: Vertical Slice Architecture

Created files:
  src/Features/Products/CreateProduct.cs     -- Command + handler + validator
  src/Features/Products/GetProduct.cs        -- Query by ID + handler
  src/Features/Products/ListProducts.cs      -- Paginated list + handler
  src/Features/Products/UpdateProduct.cs     -- Command + handler + validator
  src/Features/Products/DeleteProduct.cs     -- Command + handler
  src/Features/Products/ProductEndpoints.cs  -- IEndpointGroup, OpenAPI metadata
  src/Features/Products/ProductConfig.cs     -- EF Core configuration
  tests/Features/Products/CreateProductTests.cs
  tests/Features/Products/GetProductTests.cs
  tests/Features/Products/ListProductsTests.cs

Checklist: 9/9 | Build: PASS | Tests: PASS

All files follow your existing conventions (sealed handlers,
primary constructors, TypedResults return types).

Related

  • dotnet-init — Initialize the project and CLAUDE.md before scaffolding features
  • vertical-slice — The VSA patterns the VSA scaffold follows
  • clean-architecture — Layering rules behind the CA scaffold
  • ddd — Aggregate and domain-event patterns behind the DDD scaffold
  • project-structure — Where files belong in each architecture

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 Scaffold AI skill do?

Architecture-aware feature scaffolding for .NET 10 projects. Detects the project's architecture (VSA, Clean Architecture, DDD, Modular Monolith) and generates complete feature slices with all required layers: endpoint, handler, validator, DTOs, EF configuration, and integration tests — with the completeness checklist and per-architecture code templates every generated feature must satisfy. Use when: "scaffold", "create feature", "add feature", "new endpoint", "generate", "add entity", "scaffold a module", "add module", or when customizing generation templates or defining what a complete fea...

Why use Scaffold on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codewithmukesh/dotnet-claude-kit/tree/main/skills/scaffold. 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 Scaffold?

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?

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

Is the Scaffold 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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