Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mhrsdev/AI-Agent-Skills-Library --skill design-an-interfacegit clone --depth 1 https://github.com/mhrsdev/AI-Agent-Skills-LibraryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mhrsdev/ai-agent-skills-library/design-an-interface)<a href="https://agentmods.dev/skills/mhrsdev/ai-agent-skills-library/design-an-interface"><img src="https://agentmods.dev/badge/skills/mhrsdev/ai-agent-skills-library/design-an-interface/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mhrsdev/ai-agent-skills-library/design-an-interface"><img src="https://agentmods.dev/badge/skills/mhrsdev/ai-agent-skills-library/design-an-interface.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.00711 |
| Opus 5 | $0.00022 | $0.00356 |
| Sonnet 5 | $0.00009 | $0.00142 |
| Haiku 4.5 | $0.00004 | $0.00071 |
Grade A, and why
design-an-interface scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to api-shape-explorer — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Interface signature - types, methods, params
- Usage examples - how callers actually use it in practice
- 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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 95 lines · 43 tokens per session scan A a2596e9fa2d5
design-an-interface is a skill published in the GitHub repository mhrsdev/AI-Agent-Skills-Library (6 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 711 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to api-shape-explorer, differing in 2 lines, and is treated as a copy.
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