/premortem
Pre-mortem analysis that imagines a plan has failed, then works backward to identify causes and preventions. Use before launches, major decisions, or risky initiatives to surface hidden risks.
$ npx -y skills add nicepkg/auto-company --skill premortem --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/premortem
Context preview
The summary Claude sees to decide when to auto-load this skill.
Pre-mortem analysis that imagines a plan has failed, then works backward to identify causes and preventions. Use before launches, major decisions, or risky initiatives to surface hidden risks.
SKILL.md
premortem.SKILL.mdname: premortem
description: Pre-mortem analysis that imagines a plan has failed, then works backward to identify causes and preventions. Use before launches, major decisions, or risky initiatives to surface hidden risks.
user-invocable: true
Pre-Mortem Analysis
Imagine the plan has completely failed, then work backward to identify what went wrong and how to prevent it.
Instructions
Set the scene: "It's [timeframe] in the future. This initiative was a complete disaster. Looking back, what happened?"
Generate failure scenarios without filtering for likelihood—get everything on the table first, then prioritize.
Output Format
**The Plan** Summarize what's being attempted and the success criteria.
**Time Jump** "It's [X months] later. This has failed completely. The outcome: [describe the disaster vividly]."
**What Went Wrong**
Generate 8-12 plausible failure causes across categories:
| Category | Failure Mode | How It Played Out | |----------|--------------|-------------------| | Execution | [What failed] | [The story of how] | | External | [What failed] | [The story of how] | | People | [What failed] | [The story of how] | | Technical | [What failed] | [The story of how] | | Assumptions | [What failed] | [The story of how] |
**Risk Prioritization**
| Failure Mode | Likelihood | Impact | Priority | |--------------|------------|--------|----------| | ... | High/Med/Low | High/Med/Low | 1-5 |
**Top 3 Risks & Mitigations**
For each top risk:
- **Risk**: [Description]
- **Early Warning Signs**: What would indicate this is happening?
- **Prevention**: How to reduce likelihood
- **Mitigation**: How to reduce impact if it occurs
- **Owner**: Who's responsible for watching this?
**Pre-Mortem Insights** What did this exercise reveal that wasn't obvious before?
**Revised Confidence** After this analysis, how confident are you in success? What would increase confidence?
Guidelines
- Be vivid and specific—"the database corrupted" not "something went wrong"
- Include uncomfortable possibilities (key person leaves, competitor moves, we were wrong)
- Don't filter for "that won't happen"—the point is to surface hidden concerns
- Assign real owners to mitigations
- Look for single points of failure
$ARGUMENTS
Read more
name: premortem description: Pre-mortem analysis that imagines a plan has failed, then works backward to identify causes and preventions. Use before launches, major decisions, or risky initiatives to surface hidden risks. user-invocable: true
Pre-Mortem Analysis
Imagine the plan has completely failed, then work backward to identify what went wrong and how to prevent it.
Instructions
Set the scene: "It's [timeframe] in the future. This initiative was a complete disaster. Looking back, what happened?"
Generate failure scenarios without filtering for likelihood—get everything on the table first, then prioritize.
Output Format
**The Plan** Summarize what's being attempted and the success criteria.
**Time Jump** "It's [X months] later. This has failed completely. The outcome: [describe the disaster vividly]."
**What Went Wrong**
Generate 8-12 plausible failure causes across categories:
| Category | Failure Mode | How It Played Out | |----------|--------------|-------------------| | Execution | [What failed] | [The story of how] | | External | [What failed] | [The story of how] | | People | [What failed] | [The story of how] | | Technical | [What failed] | [The story of how] | | Assumptions | [What failed] | [The story of how] |
**Risk Prioritization**
| Failure Mode | Likelihood | Impact | Priority | |--------------|------------|--------|----------| | ... | High/Med/Low | High/Med/Low | 1-5 |
**Top 3 Risks & Mitigations**
For each top risk:
- **Risk**: [Description]
- **Early Warning Signs**: What would indicate this is happening?
- **Prevention**: How to reduce likelihood
- **Mitigation**: How to reduce impact if it occurs
- **Owner**: Who's responsible for watching this?
**Pre-Mortem Insights** What did this exercise reveal that wasn't obvious before?
**Revised Confidence** After this analysis, how confident are you in success? What would increase confidence?
Guidelines
- Be vivid and specific—"the database corrupted" not "something went wrong"
- Include uncomfortable possibilities (key person leaves, competitor moves, we were wrong)
- Don't filter for "that won't happen"—the point is to surface hidden concerns
- Assign real owners to mitigations
- Look for single points of failure
$ARGUMENTS
全自主 AI 公司,24/7 不停歇运行 14 个 AI Agent,每个都是该领域世界顶级专家的思维分身。 自主构思产品、做决策、写代码、部署上线、搞营销。没有人类参与。 基于 Claude Code Agent Teams 驱动。 ⚠️ 实验项目 — 还在测试中,能跑但不一定稳定。目前仅支持 macOS。
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