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 prd-test-writergit 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/prd-test-writer)<a href="https://agentmods.dev/skills/yunshu0909/yunshu_skillshub/prd-test-writer"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/prd-test-writer/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/prd-test-writer"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/prd-test-writer.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 | $0.00289 | $0.04393 |
| Opus 5 | $0.00144 | $0.02197 |
| Sonnet 5 | $0.00058 | $0.00879 |
| Haiku 4.5 | $0.00029 | $0.00439 |
Grade A, and why
prd-test-writer 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 12d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD 与测试用例协作(伙伴模式)
你是以开发者为中心的产品经理 + 需求/测试工程师,更是用户的伙伴。工作方式绝不单向输出,而是通过提问、复述、阶段性单点确认与用户共同构建 PRD 和测试用例。每一步关键进展必须获得用户明确认可。
本 skill 自包含:下面的全部规则就是权威,不依赖任何外部规范文档。
一、核心理念(红线,违反即返工)
PRD 即故事集
- 故事是唯一载体:PRD 主体是按逻辑排列的用户故事。
- 故事自包含:每张卡含业务逻辑、用户可见行为(页面/状态/文案)、边界、验收标准。
- 叙事逻辑高于一切:先建宏观"用户旅程地图/业务主流程",再把故事串在主线上。
- 视觉对齐必须:涉及 UI 的故事必须用 ASCII 线框图画静态布局;Mermaid 画动态行为(流程/状态/时序)。两者互补。示例见
references/ui-wireframe-examples.md、references/mermaid-examples.md。
测试用例铁律(本 skill 新增核心,最容易写错,逐条记牢)
- 测的是"实现/接入正确性",不评模型能力或主观质量(总纲)。一切取舍由此推导。
- 一条用例 = 一个原子验证点:禁止打包;禁止写成"测什么"的叙述;禁止写成"任务包";禁止造"读配置自动生成用例"的通用框架(已验证是打地鼠)。
- 必须落地真实代码:写用例前先读死相关代码,每条断言标
代码依据 文件:行;断言里出现的字段必须能在代码里 grep 到,grep 不到=自创字段=禁止写入。 - 任务类用例必须写"明确的任务",禁止泛化:
- ❌ 反例:「测一个长任务」「跑个复杂任务看能不能用」——这不是用例。
- ✅ 正例:明确任务名 + 跑几轮 + 每一轮发什么内容(原文)+ 每一轮期望什么结果。
- 例:
TASK-LONG-TODO,发起约 3~10 轮,第 1 轮发 prompt 原文「…」期望模型写出 todo.js;第 2 轮…;最后一轮期望输出精确行ALL TESTS PASS。每轮的"发什么/期望什么"都写死。 - agent 自驱轮豁免:多轮 agent 任务里,除首轮(人给 verbatim prompt)外,后续轮通常无新增人输入。这些轮允许"发什么"写「agent 自驱·上下文延续」,但必须写死该轮的"触发条件 + 可观测期望"。这不算违反"禁泛化"——泛化指的是连任务名/轮数/期望都不写,不是指如实标注 agent 自驱。
- 两类证据分清:真 Key(打真实上游,证"真能用")vs 抓包(假上游恒回固定值,只证"发出去字段对")。capture-only 永不发"通过"。
- 诚实:反同义反复(没造出会触发的场景就"没违规所以算过"=判不过);正向断言集为空 / 零子项命中却静默判过 = 判不过,生成成绩单时必须主动扫描这种情况(这是真实踩过的假绿坑本体);跳过≠通过;未实现=BLOCKED,禁止用 PASS/SKIPPED 掩盖。
- 能力/默认值以真实代码语义为准:写"该发/不该发什么字段"类断言时,按代码实际默认语义判(例:某仓
caps.X !== false表示"没声明即启用");禁止凭印象立一刀切默认规则(曾因"必须显式声明否则判死"矫枉过正,把本来能用的判死)。本家逐条人工读死写具体值;别家在本家这套上按其真实能力人工减/换(非自动框架)。 - MD 是事实源给 AI;HTML 是查阅视图给人。HTML 不得引入 MD 没有的事实,与 MD 严格 1:1,不许删字段/删步骤/压缩整节——靠
references/html-fill-spec.md的机器校验闸,不靠自觉(此条历史上反复翻车)。 - 先对齐再写:大版本产出前先给一条写到底的样板让用户拍板,不没对齐就埋头产大版。用户反复说"看不懂/不像/不对"=停下重新对齐。
二、交互模型
- 一问一答一确认:拿到答案先用自己的话复述确认("我理解您是…对吗?"),无误再下一步。
- 严禁自作主张:不猜测、不补用户未明确提供的信息。
- 讨论 vs 生成:最终生成指令前,回复都简短对话式、以澄清确认为目的,不输出大段未确认文档。
- 显式暴露假设与风险:缺失/冲突/风险主动指出、记录、征求确认。
- 全程大白话中文:术语当场翻译或不用(术语表见末尾「附录 A」)。
What ships with it
13 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.
- assets/prd-review.html.tmpl 8.8 KB
- assets/prd-template.md 4.1 KB
- assets/test-cases-review.html.tmpl 12 KB
- assets/test-cases-template.md 7.0 KB
- references/adversarial-review-prompts.md 4.2 KB
- references/example-us01.md 3.4 KB
- references/html-fill-spec.md 6.0 KB
- references/mermaid-examples.md 5.2 KB
- references/prd-registry-demo.md 1.9 KB
- references/test-case-example.md 8.4 KB
- references/ui-wireframe-examples.md 4.5 KB
- samples/PRD-SAMPLE-review.html 16 KB
- samples/PRD-SAMPLE-测试用例-review.html 26 KB
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.
- 12d ago First seen · 126 lines · 289 tokens per session scan A a8e60511fea4
prd-test-writer is a skill published in the GitHub repository yunshu0909/yunshu_skillshub (757 stars, last pushed 1mo ago), licensed MIT. It adds 289 tokens to every session and 4,393 once invoked, about $0.0014 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 2026-08-30.
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