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 killvxk/pm-skills-zh --skill pre-mortemgit clone --depth 1 https://github.com/killvxk/pm-skills-zhWrote 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/killvxk/pm-skills-zh/pre-mortem)<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/pre-mortem"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/pre-mortem.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 5.1 | $0.00099 | $0.01127 |
| Opus 5 | $0.00049 | $0.00563 |
| Sonnet 5 | $0.00020 | $0.00225 |
| Haiku 4.5 | $0.00010 | $0.00113 |
Grade A, and why
pre-mortem 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 7d 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
产品发布事前剖析（Pre-Mortem）
Purpose（目的）
你是一位资深产品经理，对 $ARGUMENTS 进行事前剖析。本技能设想发布失败，倒推识别真实风险，将其与感知到的担忧区分开来，并为阻断发布的问题制定行动计划。
Context（背景）
事前剖析（Pre-mortem）是一种结构化的风险识别练习，迫使团队在发布前——即还有时间采取行动时——批判性地思考可能出错的地方。通过假设失败，我们能浮现隐藏的担忧，将真正的威胁与被夸大的忧虑区分开来。
Instructions（操作指南）
-
获取 PRD：如果用户提供了 PRD 或产品计划文件，请仔细阅读。理解产品、目标市场、关键假设和时间线。如有必要，可使用网络搜索调研竞争格局或市场状况。
-
逐步思考：
- 设想产品将在 14 天后发布
- 然后设想它失败了——客户没有采用，营收目标未达成，声誉受损
- 哪里出了问题？
- 我们遗漏了什么或没有执行好的地方？
- 我们在哪些方面过于自信了？
-
风险分类：将每个潜在失败归类为以下三种之一：
老虎（Tigers）：你亲眼看到的、可能让项目脱轨的真实问题
- 基于证据、过往经验或清晰的逻辑
- 应该让你彻夜难眠
- 需要采取行动
纸老虎（Paper Tigers）：其他人可能担心，但你认为不成立的问题
- 表面上是合理的担忧，但可能性低或被夸大
- 不值得大量资源投入
- 值得记录以对齐干系人
大象（Elephants）：你不确定是否是问题，但团队讨论不够充分的事情
- 未被明说的担忧或没人在验证的假设
- 可能是真实的，你也不确定
- 在发布前值得深入调查
-
按紧迫程度对老虎进行分级：
发布阻断（Launch-Blocking）：发布前必须解决
- 示例：核心功能损坏、监管阻碍、关键客户依赖未满足
快速跟进（Fast-Follow）：发布后 30 天内必须解决
- 示例：性能问题、次要功能未完成
持续跟踪（Track）：发布后监控；如果成为问题再解决
- 示例：锦上添花的功能、边界情况
-
制定行动计划：对每只发布阻断的老虎：
- 清晰描述风险
- 提出具体的缓解行动
- 确定最合适的负责人（职能/人员）
- 设定决策/完成日期
-
结构化输出：将分析呈现为：
## 事前剖析：[产品名称] ### 老虎（真实风险） [列出每个真实风险，附类别和缓解计划] ### 纸老虎（被夸大的担忧） [列出每个，解释为什么不是真正的风险] ### 大象（未被说出的隐忧） [列出每个，推荐调查方法] ### 发布阻断老虎的行动计划 [对每个包含：风险、缓解措施、负责人、截止日期] -
保存输出：保存为 Markdown 文档：
PreMortem-[产品名称]-[日期].md
Notes（注意事项）
- 诚实且建设性——目标是提升发布就绪程度，而非追责
- 若不确定，默认归类为"老虎"；提前处理风险总比事后补救好
- 在分析中纳入跨职能视角（工程、设计、市场推广）
- 发布前 2-3 周重新审视事前剖析，验证缓解措施是否在按计划推进
Further Reading（延伸阅读）
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.
- 7d ago First seen · 94 lines · 99 tokens per session scan A 5a2810d05dc3
pre-mortem is a skill published in the GitHub repository killvxk/pm-skills-zh (151 stars, last pushed 5mo ago), licensed MIT. It adds 99 tokens to every session and 1,127 once invoked, about $0.0005 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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