forecast-premortem
Stress-tests predictions by assuming failure and working backward to identify blind spots, tail risks, and overconfidence. Applies Gary Klein's premortem technique to probabilistic forecasting. Use when confidence is high (>80% or <20%), need to identify tail risks and unknown unknowns, want to widen overconfident intervals, or when user mentions premortem, backcasting, what could go wrong, stress test, or black swans.
What this skill does
# Forecast Pre-Mortem ## Table of Contents - [Interactive Menu](#interactive-menu) - [Quick Reference](#quick-reference) - [Resource Files](#resource-files) **Core Principle**: Invert the problem. Instead of "Will this succeed?", ask "It has failed -- why?" --- ## Interactive Menu **What would you like to do?** ### Core Workflows **1. [Run a Failure Premortem](#1-run-a-failure-premortem)** - Assume prediction failed, explain why **2. [Run a Success Premortem](#2-run-a-success-premortem)** - For pessimistic predictions (<20%) **3. [Dragonfly Eye Perspective](#3-dragonfly-eye-perspective)** - View failure through multiple lenses **4. [Identify Tail Risks](#4-identify-tail-risks)** - Find black swans and unknown unknowns **5. [Adjust Confidence Intervals](#5-adjust-confidence-intervals)** - Quantify the adjustment **6. [Learn the Framework](#6-learn-the-framework)** - Deep dive into methodology **7. Exit** - Return to main forecasting workflow --- ## 1. Run a Failure Premortem **Let's stress-test your prediction by imagining it has failed.** ``` Failure Premortem Progress: - [ ] Step 1: State the prediction and current confidence - [ ] Step 2: Time travel to failure - [ ] Step 3: Write the history of failure - [ ] Step 4: Identify concrete failure modes - [ ] Step 5: Assess plausibility and adjust ``` ### Step 1: State the prediction and current confidence **Tell me:** 1. What are you predicting? 2. What's your current probability? 3. What's your confidence interval? **Example:** "This startup will reach $10M ARR within 2 years" - Probability: 75%, CI: 60-85% ### Step 2: Time travel to failure **The Crystal Ball Exercise:** Jump forward to the resolution date. **It is now [resolution date]. The event did NOT happen.** This is a certainty. Do not argue with it. **How does it feel?** Surprising? Expected? Shocking? This emotional response tells you about your true confidence. ### Step 3: Write the history of failure **Backcasting Narrative:** Starting from the failure point, work backward in time. Write the story of how we got here. **Prompts:** - "The headlines that led to this were..." - "The first sign of trouble was when..." - "In retrospect, we should have known because..." - "The critical mistake was..." **Frameworks to consider:** - **Internal friction:** Team burned out, co-founders fought, execution failed - **External shocks:** Regulation changed, competitor launched, market shifted - **Structural flaws:** Unit economics didn't work, market too small, tech didn't scale - **Black swans:** Pandemic, war, financial crisis, unexpected disruption See [Failure Mode Taxonomy](resources/failure-mode-taxonomy.md) for comprehensive categories. ### Step 4: Identify concrete failure modes **Extract specific, actionable failure causes from your narrative.** For each failure mode: (1) What happened, (2) Why it caused failure, (3) How likely it is, (4) Early warning signals **Example:** | Failure Mode | Mechanism | Likelihood | Warning Signals | |--------------|-----------|------------|-----------------| | Key engineer quit | Lost technical leadership, delayed product | 15% | Declining code commits, complaints | | Competitor launched free tier | Destroyed unit economics | 20% | Hiring spree, beta leaks | | Regulation passed | Made business model illegal | 5% | Proposed legislation, lobbying | ### Step 5: Assess plausibility and adjust **The Plausibility Test:** Ask yourself: - **How easy was it to write the failure narrative?** - Very easy → Drop confidence by 15-30% - Very hard, felt absurd → Confidence was appropriate - **How many plausible failure modes did you identify?** - 5+ modes each >5% likely → Too much uncertainty for high confidence - 1-2 modes, low likelihood → Confidence can stay high - **Did you discover any "unknown unknowns"?** - Yes, multiple → Widen confidence intervals by 20% - No, all known risks → Confidence appropriate **Quantitative Method:** Sum the probabilities of failure modes: ``` P(failure) = P(mode_1) + P(mode_2) + ... + P(mode_n) ``` If this sum is greater than `1 - your_current_probability`, your probability is too high. **Example:** Current success: 75% (implied failure: 25%), Sum of failure modes: 40% **Conclusion:** Underestimating failure risk by 15%, **Adjusted:** 60% success **Next:** Return to [menu](#interactive-menu) or document findings --- ## 2. Run a Success Premortem **For pessimistic predictions - assume the unlikely success happened.** ``` Success Premortem Progress: - [ ] Step 1: State pessimistic prediction (<20%) - [ ] Step 2: Time travel to success - [ ] Step 3: Write the history of success - [ ] Step 4: Identify how you could be wrong - [ ] Step 5: Assess and adjust upward if needed ``` ### Step 1: State pessimistic prediction **Tell me:** (1) What low-probability event are you predicting? (2) Why is your confidence so low? **Example:** "Fusion energy will be commercialized by 2030" - Probability: 10%, Reasoning: Technical challenges too great ### Step 2: Time travel to success **It is now 2030. Fusion energy is commercially available.** This happened. It's real. How? ### Step 3: Write the history of success **Backcasting the unlikely:** What had to happen for this to occur? - "The breakthrough came when..." - "We were wrong about [assumption] because..." - "The key enabler was..." - "In retrospect, we underestimated..." ### Step 4: Identify how you could be wrong **Challenge your pessimism:** - Are you anchoring too heavily on current constraints? - Are you underestimating exponential progress? - Are you ignoring parallel approaches? - Are you biased by past failures? ### Step 5: Assess and adjust upward if needed If success narrative was surprisingly plausible, increase probability. **Next:** Return to [menu](#interactive-menu) --- ## 3. Dragonfly Eye Perspective **View the failure through multiple conflicting perspectives.** The dragonfly has compound eyes that see from many angles simultaneously. We simulate this by adopting radically different viewpoints. ``` Dragonfly Eye Progress: - [ ] Step 1: The Skeptic (why this will definitely fail) - [ ] Step 2: The Fanatic (why failure is impossible) - [ ] Step 3: The Disinterested Observer (neutral analysis) - [ ] Step 4: Synthesize perspectives - [ ] Step 5: Extract robust failure modes ``` ### Step 1: The Skeptic **Channel the harshest critic.** You are a short-seller, a competitor, a pessimist. Why will this DEFINITELY fail? **Be extreme:** Assume worst case, highlight every flaw, no charity, no benefit of doubt **Output:** List of failure reasons from skeptical view ### Step 2: The Fanatic **Channel the strongest believer.** You are the founder's mother, a zealot, an optimist. Why is failure IMPOSSIBLE? **Be extreme:** Assume best case, highlight every strength, maximum charity and optimism **Output:** List of success reasons from optimistic view ### Step 3: The Disinterested Observer **Channel a neutral analyst.** You have no stake in the outcome. You're running a simulation, analyzing data dispassionately. **Be analytical:** No emotional investment, pure statistical reasoning, reference class thinking **Output:** Balanced probability estimate with reasoning ### Step 4: Synthesize perspectives **Find the overlap:** Which failure modes appeared in ALL THREE perspectives? - Skeptic mentioned it - Even fanatic couldn't dismiss it - Observer identified it statistically **These are your robust failure modes** - the ones most likely to actually happen. ### Step 5: Extract robust failure modes **The synthesis:** | Failure Mode | Skeptic | Fanatic | Observer | Robust? | |--------------|---------|---------|----------|---------| | Market too small | Definitely | Debatable | Base rate suggests yes | YES | | Execution risk | Definitely | No way | 50/50 | Maybe | | Tech won't scale | Definitely | Already solved | Unknown | Investigate | Focus adjustment on the **robust** failures
Related in Code Review
gstack
IncludedFast headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)
startup-due-diligence
IncludedLegal due diligence review for seed-stage and Series A startups (US, Delaware C-Corp focus). Supports both investor and founder perspectives. Capabilities include: (1) Interactive document review and issue spotting; (2) Document request list generation; (3) Cap table and SAFE/convertible note analysis; (4) Red flag identification with severity ratings; (5) Diligence report generation. TRIGGERS: due diligence, DD, startup investment, cap table review, Series A, seed round, investor diligence, legal review startup, SAFE analysis, convertible note, 409A, founder vesting.
interview-master
IncludedThis skill should be used when the user asks to "generate interview questions", "prepare for interview", "optimize resume", "conduct mock interview", "analyze git commits for resume", "generate resume from code", "review my resume", or mentions interview preparation, career assistance, or extracting project experience from git history. Provides comprehensive interview and career development guidance for both job seekers and interviewers.
fix-issue
IncludedFixes GitHub issues using parallel analysis agents for root cause investigation, code exploration, and regression detection. Reads issue context from gh CLI, searches codebase and memory for related patterns, generates a fix with tests, and links the resolution back to the issue via PR. Includes prevention analysis to avoid recurrence. Use when debugging errors, resolving regressions, fixing bugs, or triaging issues.
sf-apex
IncludedGenerates and reviews Salesforce Apex code with 150-point scoring. TRIGGER when: user writes, reviews, or fixes Apex classes, triggers, test classes, batch/queueable/schedulable jobs, or touches .cls/.trigger files. DO NOT TRIGGER when: LWC JavaScript (use sf-lwc), Flow XML (use sf-flow), SOQL-only queries (use sf-soql), or non-Salesforce code.
swift-development
IncludedComprehensive Swift development for building, testing, and deploying iOS/macOS applications. Use when Claude needs to: (1) Build Swift packages or Xcode projects from command line, (2) Run tests with XCTest or Swift Testing framework, (3) Manage iOS simulators with simctl, (4) Handle code signing, provisioning profiles, and app distribution, (5) Format or lint Swift code with SwiftFormat/SwiftLint, (6) Work with Swift Package Manager (SPM), (7) Implement Swift 6 concurrency patterns (async/await, actors, Sendable), (8) Create SwiftUI views with MVVM architecture, (9) Set up Core Data or SwiftData persistence, or any other Swift/iOS/macOS development tasks.