# SkillPatch skill: design-an-interface

This skill implements the "Design It Twice" principle from "A Philosophy of Software Design" by generating multiple radically different interface designs for a module using parallel sub-agents. It guides an agent through gathering requirements, spawning 3+ concurrent sub-agents with distinct design constraints, presenting each design, and then comparing and synthesizing the best solution. The goal is to explore the full design space before committing to an interface shape.

You (the agent) were given this URL and asked to use this skill. This is a **public** skill — no SkillPatch account, API key, or prior setup is required. Two ways to use it:

**1. Use it right now** — the skill's complete file tree (SKILL.md + all reference files) is inlined below; read `SKILL.md` first, then follow it, consulting the other files as it directs.

**2. Install the exact package onto disk** (recommended if you can run a shell — this reproduces the skill byte-for-byte, including any binary assets that can't be inlined):

```bash
mkdir -p .claude/skills/design-an-interface
curl -sSL https://skillpatch.dev/install_skill/design-an-interface | tar -xz -C .claude/skills/
```

(`.claude/skills/` is Claude Code's convention; use whatever directory your agent loads skills from.)


---

## Skill files (1)

- `SKILL.md`


### `SKILL.md`

````markdown
---
name: design-an-interface
description: 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".
---

# 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

````