Describe Design logo

Describe Design

Organization
posit-dev
describe-design

Research a codebase and create architectural documentation describing how features or systems work. Use when the user asks to: (1) Document how a feature works, (2) Create an architecture overview, (3) Explain code structure for onboarding or knowledge transfer, (4) Research and describe a system's design. Produces markdown documents with Mermaid diagrams and stable code references suitable for humans and AI agents.

Overview

Publisherposit-dev
Repositoryskills
Skill namedescribe-design
Stars
516
Forks
53
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 posit-dev on GitHub. Read the source before you install it.

Installation

Install the Describe 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/posit-dev/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/posit-dev/describe-design .claude/skills/describe-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Describe 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 Describe 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 Describe 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.

Describe Design

Research a codebase and produce an architectural document describing how features or systems work. The output is a markdown file organized for both human readers and future AI agents.

Workflow

Stage 1: Scope Definition

Understand what to document before exploring:

  1. Ask what feature, system, or component to document.
  2. Clarify the target audience (developers, AI agents, or both).
  3. Confirm the codebase location if not obvious from context.

Stage 2: Initial Exploration

Explore the codebase broadly to build a mental model. Use lightweight, fast exploration methods when available (in Claude Code, for example, use a Haiku Explore subagent):

  1. Scan directory structure and identify key entry points.
  2. Read README, config files, and existing documentation.
  3. Identify the main files and modules related to the feature.
  4. Build a mental model of codebase organization.

Present a high-level outline to the user:

## Proposed Outline

1. [Component A] - Brief description
2. [Component B] - Brief description
3. [Component C] - Brief description

* Have I correctly captured the scope of the research? Reply "yes" to continue.
* Otherwise, please let me know what I've misunderstood.

When the user confirms the scope, move on to deep research.

Stage 3: Deep Research

For each component in the approved outline:

  1. Trace code paths from entry points.
  2. Identify dependencies and interactions between components.
  3. Note configuration options and where they're defined.
  4. Find where data is stored or persisted.
  5. Build a code reference index (file paths + key function/class names).

Try to rely on the initial code exploration for much of this information. Read additional files as needed. If the scope changed considerably in Stage 2, you can engage a second code exploration subagent.

When to Stop Exploring

You're ready to draft when you can:

  • Trace the happy path — Follow a typical request/action from entry point to completion without gaps.
  • Name the boundaries — Clearly state what's in scope and what's external.
  • Draw the diagram — Sketch the architecture without placeholder boxes.
  • Answer "what talks to what?" — For each component, you know its inputs and outputs.

Signs you're not done:

  • Uncertainty: "I think this connects to..." or "probably calls..."
  • Unresolved references: Found imports/calls to modules you haven't examined.
  • Missing edges: Can't explain how data gets from component A to B.

Signs you've gone too far:

  • Reading every file in a directory instead of representative samples.
  • Tracing into external libraries or framework internals.
  • Exploring implementation details that don't affect architecture.

Stage 4: Document Draft

Generate the document following the template below. Present the draft to the user for review and iterate based on feedback. If available, use the AskUserQuestion tool to request user input on key decisions.

Stage 5: Finalize

  1. Confirm the file location before writing. You may propose a path based on repository conventions (e.g., docs/architecture/, ARCHITECTURE.md), but NEVER write the file without explicit user confirmation of the location. If the user provided a path upfront, that counts as confirmation.
  2. Write the final document to the confirmed location.

Document Template

The following template provides a starting point. Adapt it to fit the feature being documented — omit sections that don't apply, add sections for unique aspects, and adjust the structure to best serve the target audience.

markdown
# [Feature/System Name] Architecture

## Overview

[1-2 paragraph summary of what this feature/system does and why it exists]

## Architecture Diagram

```mermaid
flowchart TD
    A[Entry Point] --> B[Component]
    B --> C[Data Store]
```

## Components

### [Component Name]

**Purpose**: [What it does]

**Location**: `path/to/file.ext`

**Key Functions**:
- `functionName()` - Brief description
- `anotherFunction()` - Brief description

**Interactions**:
- Receives input from: [Component]
- Sends output to: [Component]

## Data Flow

[Description of how data moves through the system, from input to output]

## Configuration

[How features are enabled, disabled, or configured. Include file paths and
environment variables.]

## Code References

| Component | File | Key Symbols |
|-----------|------|-------------|
| Auth | `src/auth/index.ts` | `authenticate()`, `AuthConfig` |
| Cache | `src/cache/redis.ts` | `CacheManager`, `invalidate()` |

## Glossary

| Term | Definition |
|------|------------|
| [Term] | [Project-specific definition] |

Code Reference Conventions

Use stable references that survive refactoring:

  • Paths: Use relative paths from repository root (src/auth/login.ts)
  • Symbols: Reference function and class names, not line numbers
  • Format: path/to/file.ext with key symbols listed separately
  • Anchors: Use search patterns when helpful (handleAuth function in auth/)

Avoid:

  • Copying code: Never paste code into the document. Code goes stale immediately; the document should be a guide that points readers to the source. Describe what code does, then reference where to find it.
  • Line numbers: They change with every edit.
  • Absolute paths: Use repository-relative paths only.

Mermaid Diagrams

Use Mermaid for architecture visualizations:

Flowcharts for component relationships:

mermaid
flowchart TD
    A[Client] --> B[API Gateway]
    B --> C[Service]
    C --> D[(Database)]

Sequence diagrams for request flows:

mermaid
sequenceDiagram
    Client->>API: Request
    API->>Service: Process
    Service-->>API: Response
    API-->>Client: Result

Keep diagrams focused on the specific feature being documented. Avoid overcrowding with unrelated components.

Writing Guidelines

  • Describe, never copy: Explain what code does and where to find it. Readers who need implementation details will read the actual source — which is always current.
  • Structure for scanning: Use headers, tables, and lists for quick navigation.
  • Be specific: Include actual file paths, function names, and config keys.
  • Serve two audiences: Write clearly for humans; use consistent structure for AI.
  • Stay current: Note any assumptions about code state or version.

Frequently asked questions

What does the Describe Design AI skill do?

Research a codebase and create architectural documentation describing how features or systems work. Use when the user asks to: (1) Document how a feature works, (2) Create an architecture overview, (3) Explain code structure for onboarding or knowledge transfer, (4) Research and describe a system's design. Produces markdown documents with Mermaid diagrams and stable code references suitable for humans and AI agents.

Why use Describe Design on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/posit-dev/skills/tree/main/posit-dev/describe-design. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Describe 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 Describe Design?

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

Is the Describe Design AI skill free?

Yes. It is published on GitHub by posit-dev 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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