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Overview
Perform structured pre-mortem analyses by assuming a project has already failed, then systematically enumerating, categorizing, prioritizing, and mitigating plausible failure modes.
Key Advantages
1.Counteracts optimism bias by framing failure as certain and working backward from that assumption.
2.Provides a clear, repeatable process (scene setting → brainstorming → categorization → rating → prevention → monitoring).
3.Uses structured dimensions (People, Process, Technology, External) that map well to most project types.
4.Includes prioritization via likelihood × impact and focuses user attention on the top 3 risks.
5.Prompts definition of concrete mitigation actions and early warning indicators, not just abstract risks.」「Aligns well with adjacent analytical skills (e.g., inversion, second-order consequences, first
Use Cases
- Running a pre-mortem for a new product launch before go-to-market.
- Stress-testing a startup’s strategy or fundraising plan by imagining it has failed in 6–12 months.
- Assessing risks before a major software deployment, migration, or architecture change.
- Evaluating organizational change initiatives (reorgs, process overhauls, new policies) prior to rollout.
- Pre-launch review for marketing campaigns, events, or feature releases to surface hidden failure modes.
Evaluation Scores
8.5
/ 10
Reliability
8.0
Functionality
8.7
Usability
9.0
Safety
8.0
Performance
8.3
Compatibility
9.0
Based on 1 evaluation · Latest: 3/19/2026
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Evaluation History (1)
8.5/103/19/2026▼
OS: linux-arm64LLM: openai/gpt-5-nano
**Quick judgment:** This is a strong, well-scoped analytical skill that leverages a pre-mortem framing to elicit deeper, more honest risk identification than typical risk assessments. It is highly compatible with LLM reasoning capabilities and offers a clear, structured output format that will be easy for users to consume.
**Strengths & functionality:**
- Encourages a specific cognitive frame (“It has already failed”) that reduces optimism bias.
- Provides a stepwise method: set the scene → list 10+ failure reasons → categorize (People/Process/Technology/External) → rate likelihood × impact → define mitigations → define early warning signs.
- Output template is explicit and practical, including top-3 priorities and monitoring checklists.
- Trigger phrases are broad and intuitive (e.g., “pre-mortem”, “what could go wrong”, “stress test”, “what would kill this”), increasing activation reliability.
**Key risks / limitations:**
- Quality of analysis will depend heavily on how much concrete context the user supplies about their project; with vague inputs, the model may resort to generic failure modes.
- Likelihood × impact ratings are inherently subjective; the skill does not enforce grounding in quantitative data.
- If used on harmful or high-risk projects (e.g., dangerous technologies), it could indirectly help with robustness planning unless constrained by higher-level safety filters.
- No explicit guidance on handling very large or highly regulated projects where domain expertise and compliance constraints are critical.
**Recommended scenarios for use:**
- Before launching new products, features, campaigns, or major internal initiatives, to surface non-obvious risks.
- As a structured facilitation aid for teams doing risk workshops or design reviews.
- In conjunction with strategy or planning skills (e.g., inversion, second-order consequences) to turn identified risks into concrete design or process changes.
- As a recurring check (e.g., quarterly) on ongoing projects to reassess emerging failure modes and update warning indicators.
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