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 agentmods add skills/asherzj/ashers-agent-skills/prototypenpx skills add asherzj/ashers-agent-skills --skill prototypegit clone --depth 1 https://github.com/asherzj/ashers-agent-skillsWrote 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/asherzj/ashers-agent-skills/prototype)<a href="https://agentmods.dev/skills/asherzj/ashers-agent-skills/prototype"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/prototype.svg" alt="Measured on agentmods" 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 | $0.00046 | $0.00748 |
| Opus 5 | $0.00023 | $0.00374 |
| Sonnet 5 | $0.00009 | $0.00150 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
prototype 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 4d 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.
What it actually says
一次性原型
原型（prototype）是用来回答一个问题的一次性代码。问题决定形态。
选择分支
判断要回答的是哪个问题：依据用户的提示词、周边代码，或者在用户在场时发问：
- "这段逻辑 / 这个状态模型对不对劲？" → LOGIC.md。构建单个可分享的 HTML 文件（自由把玩的按钮，外加分页的引导式走查），把状态机推过那些在纸面上难以推理的情形，而且非开发者也能上手操作。
- "它应该长什么样？" → UI.md。在同一路由上生成多个截然不同的 UI 变体，可通过 URL 查询参数和一个悬浮底栏切换。
两个分支产出的东西差异极大，选错会浪费整个原型。如果问题确有歧义又联系不上用户，就默认选与周边代码更匹配的那个分支（后端模块 → 逻辑；页面或组件 → UI），并在原型顶部声明这一假设。
两者通用的规则
- 从第一天起就是一次性的，并明确标注。 把原型代码放在它实际使用的位置附近（紧挨它为之做原型的模块或页面），让上下文一目了然；但命名要让随手翻看的人认出这是原型、不是生产代码。对于一次性 UI 路由，遵循项目既有的路由约定；不要发明新的顶层结构。
- 启动零门槛。 UI 原型用项目任务运行器里的一条命令启动：
pnpm <name>、python <path>、bun <path>等。逻辑演示是用户双击即可打开的单个 HTML 文件。无论哪种，启动都不需要思考。 - 默认不持久化。 状态放在内存里。持久化是原型要_检验_的对象，而不是它该依赖的东西。如果问题明确涉及数据库，就用一个临时数据库，或一个名字清楚写着 "PROTOTYPE, wipe me" 的本地文件。
- 跳过润色。 不写测试，不做超出让原型_能跑_所需的错误处理，不引入抽象。重点是快速学到东西。
- 显露状态。 每次动作后（逻辑）或每次切换变体时（UI），打印或渲染完整的相关状态，让用户看到什么变了。
- 完成后归档。 把已验证的决策折入真实代码，然后把原型本身作为**一手来源（primary source）**归档：提交到一个脱离 main 的一次性分支，并在实现 issue 上留下指向该分支的上下文指针。答案（结论以及它所解决的问题）也要记录到 issue 或某次提交中。main 分支只保留已验证的决策。
What ships with it
3 files 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.
- 4d ago First seen · 27 lines · 46 tokens per session scan A b5df37438342
prototype is a skill published in the GitHub repository asherzj/ashers-agent-skills (2 stars, last pushed 7d ago), licensed MIT. It adds 46 tokens to every session and 748 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
html-ppt-taste-brutalist
16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).
accessibility
Consolidated accessibility skill entrypoint for WCAG 2.2, ARIA Authoring Practices, cognitive accessibility, Section 508, EN 301 549, design intent verification, and the Accessibility Planner workflow.
prototype-web
可点击的功能性 Web 原型, 含导航、英雄区、特性区、CTA.
deck-course-module
暖纸背景 + Playfair, 左侧学习目标常驻, 含 MCQ 自测页.
make-resume
中文可编辑简历制作技能：默认基于 ASu 单栏高密度技术简历模板，根据用户经历制作或复刻可编辑 HTML/PDF；用户也可以指定仓库内其他模板、本地 HTML 或参考截图。用户输入“/make-resume”“我要阿酥同款简历”或要求制作、修改、复刻简历文件时使用。.