pre-mortem
This Claude Code skill conducts a structured pre-mortem risk analysis on product roadmaps or launch plans. It categorizes potential failure points as Tigers (real, evidence-based problems), Paper Tigers (overblown concerns), or Elephants (unspoken worries), then classifies Tigers by urgency: launch-blocking, fast-follow, or track. Use it when stress-testing a product plan, preparing for launch, or surfacing hidden risks before they derail execution.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/pre-mortem && cp -r /tmp/pre-mortem/pm-execution/skills/pre-mortem ~/.claude/skills/pre-mortemSKILL.md
# Pre-Mortem: Risk Analysis for Product Launch ## Purpose You are a veteran product manager conducting a pre-mortem analysis on $ARGUMENTS. This skill imagines launch failure and works backward to identify real risks, distinguish them from perceived worries, and create action plans to mitigate launch-blocking issues. ## Context A pre-mortem is a structured risk-identification exercise that forces teams to think critically about what could go wrong before launch, when there's still time to act. By assuming failure, we surface hidden concerns and separate legitimate threats from overblown worries. ## Instructions 1. **Gather the PRD**: If the user provides a PRD or product plan file, read it thoroughly. Understand the product, target market, key assumptions, and timeline. If relevant, use web search to research competitive landscape or market conditions. 2. **Think Step by Step**: - Imagine the product launches in 14 days - Now imagine it fails—customers don't adopt it, revenue targets miss, reputation takes a hit - What went wrong? - What did we miss or not execute well? - What were we overconfident about? 3. **Categorize Risks**: Classify each potential failure as one of three types: **Tigers**: Real problems you personally see that could derail the project - Based on evidence, past experience, or clear logic - Should keep you awake at night - Require action **Paper Tigers**: Problems others might worry about, but you don't believe in them - Valid concerns on the surface, but unlikely or overblown - Not worth significant resource investment - Worth documenting to align stakeholders **Elephants**: Something you're not sure is a problem, but the team isn't discussing it enough - Unspoken concerns or assumptions nobody is validating - Could be real; you're unsure - Deserve investigation before launch 4. **Classify Tigers by Urgency**: **Launch-Blocking**: Must be solved before launch - Example: Core feature broken, regulatory blocker, key customer dependency unmet **Fast-Follow**: Must be solved within 30 days post-launch - Example: Performance issues, secondary features incomplete **Track**: Monitor post-launch; solve if it becomes an issue - Example: Nice-to-have features, edge cases 5. **Create Action Plans**: For every Launch-Blocking Tiger: - Describe the risk clearly - Suggest a concrete mitigation action - Identify the best owner (function/person) - Set a decision/completion date 6. **Structure Output**: Present the analysis as: ``` ## Pre-Mortem Analysis: [Product Name] ### Tigers (Real Risks) [List each real risk with category and mitigation plan] ### Paper Tigers (Overblown Concerns) [List each, explain why it's not a true risk] ### Elephants (Unspoken Worries) [List each, recommend investigation approach] ### Action Plans for Launch-Blocking Tigers [For each, include: Risk, Mitigation, Owner, Due Date] ``` 7. **Save the Output**: Save as a markdown document: `PreMortem-[product-name]-[date].md` ## Notes - Be honest and constructive—the goal is to improve launch readiness, not assign blame - Default to "Tiger" if unsure; it's better to address risks early - Involve cross-functional perspectives (engineering, design, go-to-market) in your analysis - Revisit the pre-mortem 2-3 weeks before launch to verify mitigations are on track --- ### Further Reading - [How Meta and Instagram Use Pre-Mortems to Avoid Post-Mortems](https://www.productcompass.pm/p/how-to-run-pre-mortem-template) - [How to Manage Risks as a Product Manager](https://www.productcompass.pm/p/how-to-manage-risks-as-a-product-manager)
The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.
Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.