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 commands/killvxk/pm-skills-zh/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/commands/killvxk/pm-skills-zh/pre-mortem)<a href="https://agentmods.dev/commands/killvxk/pm-skills-zh/pre-mortem"><img src="https://agentmods.dev/badge/commands/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 | $0.00030 | $0.01104 |
| Opus 5 | $0.00015 | $0.00552 |
| Sonnet 5 | $0.00006 | $0.00221 |
| Haiku 4.5 | $0.00003 | $0.00110 |
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 5d 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
/pre-mortem -- 发布前风险分析
设想你的发布已经失败，然后倒推失败原因。本命令使用"老虎/纸老虎/房间里的大象"框架，识别真实风险并制定缓解计划。
Invocation（调用示例）
/pre-mortem [粘贴或上传 PRD、发布计划或功能规格]
/pre-mortem 我们下个月要上线自助账单门户
Workflow（工作流程）
Step 1：接收计划
接受任意格式：PRD（产品需求文档）、功能规格、发布计划、项目简报或口头描述。提供的细节越多，风险分析越精准。
Step 2：识别风险
应用 pre-mortem 技能：
设想产品已上线并失败，跨维度生成风险列表：
- 技术层面：性能、扩展性、集成失败、数据问题
- 用户层面：采用壁垒、可用性问题、未满足的期望
- 业务层面：营收影响、竞争响应、市场时机
- 运营层面：支持负荷、文档缺口、培训需求
- 依赖层面：第三方服务、跨团队交接、合规监管
Step 3：风险分类
对每个风险进行分类：
老虎（Tigers） — 真实存在、可能导致失败的实质性风险
- 评估严重程度：发布阻断 / 快速跟进 / 持续跟踪
- 发布阻断的老虎：必须立即制定缓解措施
- 快速跟进的老虎：计划在发布后第一个 Sprint 内解决
- 持续跟踪的老虎：监控但不因此延迟发布
纸老虎（Paper Tigers） — 看起来吓人但其实被夸大的风险
- 解释为何该担忧是可控的
- 说明在什么情况下它可能升级为真正的老虎
房间里的大象（Elephants） — 团队心知肚明却避而不谈的风险
- 浮现政治性、组织性或令人不舒服的风险
- 以建设性方式提出，并附上建议的对话切入点
Step 4：生成事前剖析报告
## 事前剖析：[功能/发布]
**日期**: [今天]
**状态**: [草稿 / 已评审]
### 风险摘要
- **老虎**: [数量]（[发布阻断]，[快速跟进]，[持续跟踪]）
- **纸老虎**: [数量]
- **大象**: [数量]
### 发布阻断的老虎
| # | 风险 | 可能性 | 影响 | 缓解措施 | 负责人 | 截止日期 |
|---|------|--------|------|---------|-------|---------|
### 快速跟进的老虎
| # | 风险 | 可能性 | 影响 | 计划应对方案 | 负责人 |
|---|------|--------|------|------------|-------|
### 持续跟踪的老虎
[发布后需监控的风险，附触发条件]
### 纸老虎
[看起来严重但实际可控的担忧——附理由]
### 房间里的大象
[团队应该讨论却一直回避的不适真相]
### 上线检查清单
- [ ] 所有发布阻断的老虎已缓解
- [ ] 快速跟进计划已记录并分配负责人
- [ ] 持续跟踪老虎的监控机制已就位
- [ ] 回滚方案已制定
- [ ] 支持团队已完成简报
保存为 Markdown 格式。
Step 5：提供后续建议
- "需要我在 PRD 中补充风险缓解措施吗？"
- "需要我针对风险最高的区域创建测试场景吗？"
- "需要我根据这些发现起草发布检查清单吗？"
Notes（注意事项）
- 最佳事前剖析时机是计划完成 80% 时——早到足以改变方向，晚到足以有实质内容可分析
- 不要只停留在显而易见的风险——最危险的风险往往是没人提及的那些
- 房间里的大象是价值最高的输出——浮现团队一直回避讨论的内容
- 对每只老虎，缓解措施必须具体且可落实到人，而不是"要小心"
- 如果事前剖析发现发布阻断的老虎太多，建议延期或分阶段发布
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.
- 5d ago First seen · 105 lines · 30 tokens per session scan A 24034c1e1b54
pre-mortem is a command published in the GitHub repository killvxk/pm-skills-zh (151 stars, last pushed 5mo ago), licensed MIT. It adds 30 tokens to every session and 1,104 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.