Mmcp.market

prioritize-assumptions skill

by phuryn·phuryn/pm-skills·27k stars·MIT

Prioritize assumptions using an Impact × Risk matrix and suggest experiments for each. Use when triaging a list of assumptions, deciding what to test first, or applying the assumption prioritization canvas.

A100/100content scan

Is the prioritize-assumptions skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the prioritize-assumptions skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/phuryn/pm-skills.git /tmp/pm-skills
mkdir -p ~/.claude/skills
cp -r /tmp/pm-skills/pm-product-discovery/skills/prioritize-assumptions ~/.claude/skills/prioritize-assumptions
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Prioritize Assumptions

Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.

Context

You are helping prioritize assumptions for $ARGUMENTS.

If the user provides files with assumptions or research data, read them first.

Domain Context

ICE works well for assumption prioritization: Impact (Opportunity Score × # Customers) × Confidence (1–10) × Ease (1–10). Opportunity Score = Importance × (1 − Satisfaction), normalized to 0–1 (Dan Olsen). RICE splits Impact into Reach × Impact separately: (R × I × C) / E. See the prioritization-frameworks skill for full formulas and templates.

Instructions

The user will provide a list of assumptions to prioritize. Apply the following framework:

  1. For each assumption, evaluate two dimensions:
  • Impact: The value created by validating this assumption AND the number of customers affected (in ICE: Impact = Opportunity Score × # Customers)
  • Risk: Defined as (1 - Confidence) × Effort
  1. Categorize each assumption using the Impact × Risk matrix:
  • Low Impact, Low Risk → Defer testing until higher-priority assumptions are addressed
  • High Impact, Low Risk → Proceed to implementation (low risk, high reward)
  • Low Impact, High Risk → Reject the idea (not worth the investment)
  • High Impact, High Risk → Design an experiment to test it
  1. For each assumption requiring testing, suggest an experiment that:
  • Maximizes validated learning with minimal effort
  • Measures actual behavior, not opinions
  • Has a clear success metric and threshold
  1. Present results as a prioritized matrix or table.

Think step by step. Save as markdown if the output is substantial.

Further Reading

  • Assumption Prioritization Canvas: How to Identify And Test The Right Assumptions
  • Continuous Product Discovery Masterclass (CPDM) (video course)

More skills from phuryn/pm-skills

  • Aab-test-analysisAnalyze 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.
  • Aanalyze-feature-requestsAnalyze and prioritize a list of feature requests by theme, strategic alignment, impact, effort, and risk. Use when reviewing customer feature requests, triaging a backlog, or making prioritization decisions.
  • Aansoff-matrixGenerate an Ansoff Matrix analysis mapping growth strategies across market penetration, market development, product development, and diversification. Use when considering growth options, planning market expansion, or evaluating strategic growth paths.
  • Abeachhead-segmentIdentify the first beachhead market segment for a product launch. Evaluates segments against burning pain, willingness to pay, winnable market share, and referral potential. Use when choosing a first market, targeting an initial customer segment, or planning market entry strategy.
  • Abrainstorm-experiments-existingDesign experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
  • Abrainstorm-experiments-newDesign lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
  • Abrainstorm-ideas-existingBrainstorm product ideas for an existing product using multi-perspective ideation from PM, Designer, and Engineer viewpoints. Use when generating new feature ideas, brainstorming solutions for an identified opportunity, or ideating with a product trio.
  • Abrainstorm-ideas-newBrainstorm feature ideas for a new product in initial discovery from PM, Designer, and Engineer perspectives. Use when starting product discovery for a new product, exploring features for a startup idea, or doing initial ideation.
  • Abrainstorm-okrsBrainstorm 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.
  • Abusiness-modelGenerate a Business Model Canvas with all 9 building blocks. Use when creating a business model, documenting how a business creates value, or analyzing an existing business model.
  • Acode-reviewReview code for actionable defects. Correctness is the core; performance and security are optional sub-cases of the same engine. Anchors on agreements between participants across a boundary, forces a violating execution, and refutes every candidate before reporting. Use when asked to review changes, find bugs, audit a codebase, or check whether a fix is safe.
  • Acohort-analysisPerform 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.

All agent skills → · MCP servers