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 yunshu0909/yunshu_skillshub --skill readable-outputgit clone --depth 1 https://github.com/yunshu0909/yunshu_skillshubWrote 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/yunshu0909/yunshu_skillshub/readable-output)<a href="https://agentmods.dev/skills/yunshu0909/yunshu_skillshub/readable-output"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/readable-output/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/yunshu0909/yunshu_skillshub/readable-output"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/readable-output.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00237 | $0.04347 |
| Opus 5.5 | $0.00095 | $0.01739 |
| Sonnet 5.5 | $0.00047 | $0.00869 |
| Haiku 4.5 | $0.00024 | $0.00435 |
Grade A, and why
readable-output 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
输出工程：让 AI 输出长内容时按清单想一遍
工具适配
文中的 AskUserQuestion 使用当前平台的结构化询问工具；没有该工具时，用一条消息问清同样的四个维度。用户已明确的参数直接继承；工具名不同不阻断写作。
综合写作理论（Zinsser《On Writing Well》/ Heath《Made to Stick》/ Rosenfeld《Information Architecture》）+ 学习科学（Sweller CLT / Brown《Make It Stick》/ Wiggins backward design）+ 大厂文档实践（Stripe / GitLab / Linear / Anthropic / Google SRE）三方交叉验证。
元原则（贴在最顶层，每次开始前默念）
- 如果你自己读完没有获得新认知，读者也不会有。 写作是 thinking on paper，不是模板填空。
- 终点是"读者下次能用"，不是"读者这次能懂"。 Backward design：先想读者读完手里多了什么"可复用的工具"（一张对照表 / 一个判断框架 / 一条决策路径 / 一个口诀），再倒推写什么。
- 不列菜单——给推荐 + 一句话理由。 AI 喜欢"列出 5 个选项让你选"，但好文档敢于 opinionated。
触发条件
触发（需要给人读的中长 HTML 内容）：
- "做个复盘 / 总结 / 汇总 / 整理"
- "做个教程 / 学习指南 / 上手文档"
- "把 X 讲清楚 / 整理成可读的 HTML"
- "做份报告 / 说明文档"
- 任何要求产出 > 500 字的结构化阅读内容
不触发（短输出 / 操作型 / 非 HTML 需求）：
- 写代码 / 修 bug / 改文件
- 一两句话回答
- 代码 diff / 短列表
- 用户明确说"用 markdown 答" / "聊天里直接说"
- PRD（用 prd-test-writer）/ 文章写作（用 writing-assistant）/ 起名（用 product-naming）
重要：本 skill 产出只有 HTML 一种格式。如果用户要 markdown，不用本 skill。
运作模式
触发
↓
阶段 1：强制用 AskUserQuestion 一次性问 4 个关键问题
↓
用户选完
↓
阶段 2-6 在 AI 心里走（不对用户念稿）
↓
产出 HTML，顶部加「本次配置」确认条，浏览器自动打开
例外（跳过阶段 1 的提问）：
- 用户在 prompt 里已经明确所有 4 个参数（如"给 PM 看的 5K 字复盘，故事化风格"）
- 用户明确说 "快速做" / "随便给我一份" / "别问"
阶段 1（强制）：用 AskUserQuestion 一次性问 4 个
必须用 AskUserQuestion 工具发起一次询问，包含下面 4 个问题。问题文案和选项可微调但维度不能换。
问题 1：给谁看？（受众）
| 选项 | 说明 |
|---|---|
| PM / 产品经理 | 偏故事、对标、决策、可复用框架 |
| 工程师 / 技术新人 | 含代码片段、技术决策、动手指南 |
| 高管 / 决策者 | TL;DR + 数据 + 投/不投建议 |
| 自己 / 备忘 | 时间线 + 笔记式、备查 |
| 外部用户 / 客户 | 教程式、概念建梯 |
问题 2：读完拿什么？（终点）
| 选项 | 说明 |
|---|---|
| 决策依据 | 看完能 go/no/hold |
| 操作步骤 | 看完能动手 |
| 概念理解 | 看完知道是什么 |
| 故事共鸣 | 看完有感触 |
| 备忘归档 | 看完之后能查 |
问题 3：要详到什么程度？
设计原因：字数是后验感受、且常常承诺不到（素材撑不到时硬卡字数会注水）。所以字数不进用户视图，只承诺用户能感知的两件事——「包含什么」和「阅读时间」。
| 选项 | 包含什么 | 阅读时间 |
|---|---|---|
| 速读卡 | TL;DR + 1 个核心框架 / 1 张主图，能转发 | 3 分钟 |
| 标准版（默认推荐） | 核心点 + 故事 + 反例 + 出口 | 5-8 分钟 |
| 教学版 | + 背景延伸 + 多视角对照 + 可复用框架 + 边界声明 | 20-30 分钟 |
| AI 自选 | 按"受众耐受时间 × 素材独立信息点数"反推 | — |
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.
- 1mo ago Changed · +8 lines · +3 tokens per session 9924c765c687
- 2mo ago First seen · 300 lines · 234 tokens per session scan A 8fcf0e52618e
readable-output is a skill published in the GitHub repository yunshu0909/yunshu_skillshub (789 stars, last pushed 26d ago), licensed MIT. It adds 237 tokens to every session and 4,347 once invoked, about $0.0009 per session on Opus 5.5. 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 2026-08-30.
readable-output compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| readable-outputyunshu0909/yunshu_skillshub | Skill | 789 | 26d ago | MIT | A | 237 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |