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Api Shape Explorer

CommunityPopular
zebbern
api-shape-explorer

Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

Overview

Publisherzebbern
Repositoryclaude-code-guide
Skill nameapi-shape-explorer
Stars
4.6K
Forks
464
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 zebbern on GitHub. Read the source before you install it.

Installation

Install the Api Shape Explorer 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/zebbern/claude-code-guide.git /tmp/claude-code-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-guide/skills/api-shape-explorer .claude/skills/api-shape-explorer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Api Shape Explorer 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 Api Shape Explorer 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 Api Shape Explorer 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.

Design an Interface

Based on "Design It Twice" from "A Philosophy of Software Design": your first idea is unlikely to be the best. Generate multiple radically different designs, then compare.

Workflow

1. Gather Requirements

Before designing, understand:

  • What problem does this module solve?
  • Who are the callers? (other modules, external users, tests)
  • What are the key operations?
  • Any constraints? (performance, compatibility, existing patterns)
  • What should be hidden inside vs exposed?

Ask: "What does this module need to do? Who will use it?"

2. Generate Designs (Parallel Sub-Agents)

Spawn 3+ sub-agents simultaneously using Task tool. Each must produce a radically different approach.

Prompt template for each sub-agent:

Design an interface for: [module description]

Requirements: [gathered requirements]

Constraints for this design: [assign a different constraint to each agent]
- Agent 1: "Minimize method count - aim for 1-3 methods max"
- Agent 2: "Maximize flexibility - support many use cases"
- Agent 3: "Optimize for the most common case"
- Agent 4: "Take inspiration from [specific paradigm/library]"

Output format:
1. Interface signature (types/methods)
2. Usage example (how caller uses it)
3. What this design hides internally
4. Trade-offs of this approach

3. Present Designs

Show each design with:

  1. Interface signature - types, methods, params
  2. Usage examples - how callers actually use it in practice
  3. What it hides - complexity kept internal

Present designs sequentially so user can absorb each approach before comparison.

4. Compare Designs

After showing all designs, compare them on:

  • Interface simplicity: fewer methods, simpler params
  • General-purpose vs specialized: flexibility vs focus
  • Implementation efficiency: does shape allow efficient internals?
  • Depth: small interface hiding significant complexity (good) vs large interface with thin implementation (bad)
  • Ease of correct use vs ease of misuse

Discuss trade-offs in prose, not tables. Highlight where designs diverge most.

5. Synthesize

Often the best design combines insights from multiple options. Ask:

  • "Which design best fits your primary use case?"
  • "Any elements from other designs worth incorporating?"

Evaluation Criteria

From "A Philosophy of Software Design":

Interface simplicity: Fewer methods, simpler params = easier to learn and use correctly.

General-purpose: Can handle future use cases without changes. But beware over-generalization.

Implementation efficiency: Does interface shape allow efficient implementation? Or force awkward internals?

Depth: Small interface hiding significant complexity = deep module (good). Large interface with thin implementation = shallow module (avoid).

Anti-Patterns

  • Don't let sub-agents produce similar designs - enforce radical difference
  • Don't skip comparison - the value is in contrast
  • Don't implement - this is purely about interface shape
  • Don't evaluate based on implementation effort

Frequently asked questions

What does the Api Shape Explorer AI skill do?

Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

Why use Api Shape Explorer on TypingMind?

Because you install it once and use it with any model. Api Shape Explorer 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 Api Shape Explorer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zebbern/claude-code-guide/tree/main/skills/api-shape-explorer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Api Shape Explorer?

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 Api Shape Explorer?

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

Is the Api Shape Explorer AI skill free?

Yes. It is published on GitHub by zebbern 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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