assumption-mapper
Assumption Mapper extracts untested assumptions from product briefs and PRDs across four categories (desirability, feasibility, viability, usability), scores each by confidence and impact, and prioritizes validation efforts. Use this skill when reviewing product plans for hidden risks, auditing requirements documents, or validating strategic decisions before development begins.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/assumption-mapper && cp -r /tmp/assumption-mapper/plugins/pm-discovery/skills/assumption-mapper ~/.claude/skills/assumption-mapperSKILL.md
# Assumption Mapper Skill Surface and prioritize the untested assumptions embedded in any product plan before development begins. ## Required Inputs Ask the user for these if not provided: - **Product brief, PRD, or concept description** (even rough notes work) - **Stage** (concept / discovery / pre-build / post-launch — affects which assumptions matter most) ## Process 1. Read the provided brief, PRD, or concept description 2. Extract assumptions across four categories: - **Desirability** (do users want this?) - **Feasibility** (can we build it?) - **Viability** (will it sustain the business?) - **Usability** (can users actually use it?) 3. Score each assumption: - Confidence (1-5): How sure are we this is true? - Impact (1-5): How badly does the plan fail if this assumption is wrong? - Priority = Impact − Confidence (higher = test first) 4. **Validate completeness** — Ensure at least one assumption per category. If a category is empty, re-read the brief looking specifically for that type. 5. Output a ranked list with recommended validation methods ## Output Structure ### Assumption Map: [Feature/Product Name] | Assumption | Category | Confidence | Impact | Priority | Validation Method | |------------|----------|------------|--------|----------|-------------------| | [assumption] | [type] | [1-5] | [1-5] | [score] | [method] | #### Critical Assumptions (Impact 4+ and Confidence 2 or below) [Flagged items with detailed validation recommendations] #### Top 3 Assumptions to Validate First [Detailed recommendations including specific research method, estimated effort, and what the result would change] ## Example (Partial) Input: *"We're building a self-serve onboarding flow to reduce time-to-value for SMB customers."* | Assumption | Category | Confidence | Impact | Priority | Validation Method | |------------|----------|------------|--------|----------|-------------------| | SMB users can complete onboarding without human help | Usability | 2 | 5 | 3 | Unmoderated usability test (n=8) | | Faster onboarding correlates with higher retention | Viability | 3 | 4 | 1 | Cohort analysis of current onboarding times vs. 90-day retention | | The current onboarding is the primary reason for slow time-to-value | Desirability | 2 | 4 | 2 | User interviews with recent churned SMB accounts | ## Anti-Patterns - [ ] Do not only surface desirability assumptions — feasibility and viability assumptions are equally likely to kill a product and are often overlooked - [ ] Do not assign high confidence to an assumption just because it hasn't been challenged yet — absence of evidence is not evidence - [ ] Do not recommend "user interviews" as the validation method for every assumption — some assumptions require quantitative data, competitive analysis, or technical spikes - [ ] Do not list assumptions that cannot be tested — every assumption in the map must have a plausible validation method, or it should be flagged as unknowable and treated as a risk ## Quality Checks - [ ] At least one assumption per category (Desirability, Feasibility, Viability, Usability) - [ ] All Impact 4+ / Confidence 2− assumptions flagged as CRITICAL - [ ] Each validation method is specific (not just "do research" — name the method and sample size) - [ ] Priority scores are consistent (Impact − Confidence, higher = more urgent)
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.