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Codebase Design

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mattpocock
codebase-design

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

Overview

Publishermattpocock
Repositoryskills
Skill namecodebase-design
Stars
264.4K
Forks
22.3K
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Codebase Design 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/mattpocock/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/engineering/codebase-design .claude/skills/codebase-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codebase Design 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 Codebase Design 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 Codebase Design 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.

Codebase Design

Design deep modules: a lot of behaviour behind a small interface, placed at a clean seam, testable through that interface. Use this language and these principles wherever code is being designed or restructured. The aim is leverage for callers, locality for maintainers, and testability for everyone.

Glossary

Use these terms exactly: don't substitute "component," "service," "API," or "boundary." Consistent language is the whole point.

Module: anything with an interface and an implementation. Deliberately scale-agnostic: a function, class, package, or tier-spanning slice. Avoid: unit, component, service.

Interface: everything a caller must know to use the module correctly: the type signature, but also invariants, ordering constraints, error modes, required configuration, and performance characteristics. Avoid: API, signature (too narrow, they refer only to the type-level surface).

Implementation: what's inside a module, its body of code. Distinct from Adapter: a thing can be a small adapter with a large implementation (a Postgres repo) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise.

Depth: leverage at the interface. The amount of behaviour a caller (or test) can exercise per unit of interface they have to learn. A module is deep when a large amount of behaviour sits behind a small interface, shallow when the interface is nearly as complex as the implementation.

Seam (Michael Feathers): a place where you can alter behaviour without editing in that place; the location at which a module's interface lives. Where to put the seam is its own design decision, distinct from what goes behind it. Avoid: boundary (overloaded with DDD's bounded context).

Adapter: a concrete thing that satisfies an interface at a seam. Describes role (what slot it fills), not substance (what's inside).

Leverage: what callers get from depth. More capability per unit of interface they learn. One implementation pays back across N call sites and M tests.

Locality: what maintainers get from depth. Change, bugs, knowledge, and verification concentrate in one place rather than spreading across callers. Fix once, fixed everywhere.

Deep vs shallow

Deep module = small interface + lots of implementation:

┌─────────────────────┐
│   Small Interface   │  ← Few methods, simple params
├─────────────────────┤
│                     │
│  Deep Implementation│  ← Complex logic hidden
│                     │
└─────────────────────┘

Shallow module = large interface + little implementation (avoid):

┌─────────────────────────────────┐
│       Large Interface           │  ← Many methods, complex params
├─────────────────────────────────┤
│  Thin Implementation            │  ← Just passes through
└─────────────────────────────────┘

When designing an interface, ask:

  • Can I reduce the number of methods?
  • Can I simplify the parameters?
  • Can I hide more complexity inside?

Principles

  • Depth is a property of the interface, not the implementation. A deep module can be internally composed of small, mockable, swappable parts; they just aren't part of the interface. A module can have internal seams (private to its implementation, used by its own tests) as well as the external seam at its interface.
  • The deletion test. Imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
  • The interface is the test surface. Callers and tests cross the same seam. If you want to test past the interface, the module is probably the wrong shape.
  • One adapter means a hypothetical seam. Two adapters means a real one. Don't introduce a seam unless something actually varies across it.

Designing for testability

Good interfaces make testing natural:

  1. Accept dependencies, don't create them.

    typescript
    // Testable
    function processOrder(order, paymentGateway) {}
    
    // Hard to test
    function processOrder(order) {
      const gateway = new StripeGateway();
    }
  2. Return results, don't produce side effects.

    typescript
    // Testable
    function calculateDiscount(cart): Discount {}
    
    // Hard to test
    function applyDiscount(cart): void {
      cart.total -= discount;
    }
  3. Small surface area. Fewer methods = fewer tests needed. Fewer params = simpler test setup.

Relationships

  • A Module has exactly one Interface (the surface it presents to callers and tests).
  • Depth is a property of a Module, measured against its Interface.
  • A Seam is where a Module's Interface lives.
  • An Adapter sits at a Seam and satisfies the Interface.
  • Depth produces Leverage for callers and Locality for maintainers.

Rejected framings

  • Depth as ratio of implementation-lines to interface-lines (Ousterhout): rewards padding the implementation. We use depth-as-leverage instead.
  • "Interface" as the TypeScript interface keyword or a class's public methods: too narrow: interface here includes every fact a caller must know.
  • "Boundary": overloaded with DDD's bounded context. Say seam or interface.

Going deeper

  • Deepening a cluster given its dependencies, see DEEPENING.md: dependency categories, seam discipline, and replace-don't-layer testing.
  • Exploring alternative interfaces, see DESIGN-IT-TWICE.md: spin up parallel sub-agents to design the interface several radically different ways, then compare on depth, locality, and seam placement.

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 Codebase Design AI skill do?

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

Why use Codebase Design on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mattpocock/skills/tree/main/skills/engineering/codebase-design. 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 Codebase Design?

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 Codebase Design?

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

Is the Codebase Design AI skill free?

Yes. It is published on GitHub by mattpocock 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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