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neurofoo

premortem

by neurofoo

Agent Skills for Claude Code and OpenCode

31🍴 5📅 Jan 22, 2026

SKILL.md


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:

CategoryFailure ModeHow 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 ModeLikelihoodImpactPriority
...High/Med/LowHigh/Med/Low1-5

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

Score

Total Score

55/100

Based on repository quality metrics

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