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Prioritize Assumptions

Pawel HurynCommunityCreated by Pawel Huryn

  • Skill
  • Research and analysis
  • Business and productivity
  • MIT
  • No issues found
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From the author

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.
— the description by Pawel Huryn

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a systematic approach to triaging product assumptions. By evaluating assumptions against Impact and Risk dimensions, it helps teams focus their efforts on high-value, high-risk areas that require immediate validation. It guides users through categorizing assumptions into a matrix and suggests actionable, low-effort experiments to gather empirical data, ensuring product development decisions are based on validated learning rather than opinion.

What it does

  • Evaluates assumptions based on Impact and Risk dimensions.
  • Categorizes assumptions into a four-quadrant Impact vs. Risk matrix.
  • Suggests lean, high-learning experiments for high-risk assumptions.
  • Provides a framework for prioritizing product discovery efforts.
  • Defines success metrics and thresholds for proposed experiments.

When to use it

  • When you have a long list of product assumptions and need to decide what to test first.
  • During the product discovery phase to reduce uncertainty.
  • When applying the assumption prioritization canvas to a new feature or product idea.
  • When you need to justify development focus based on validated learning.

Advantages

  • Reduces wasted effort by identifying low-impact, high-risk ideas to reject.
  • Focuses team energy on high-impact assumptions that carry the most uncertainty.
  • Promotes evidence-based decision making over subjective opinion.
  • Encourages lean experimentation with minimal resource investment.

Usage examples

“Prioritize these assumptions for our new mobile checkout feature: users prefer Apple Pay, users find the current flow too long, and users want guest checkout.”
The agent will analyze these assumptions, plot them on an Impact vs. Risk matrix, and recommend which ones to test first with specific experiment designs.
“I have a list of assumptions in this document. Please prioritize them using the Impact vs. Risk framework.”
The agent will read the document, evaluate each assumption, and output a prioritized table with recommended actions for each.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/phuryn/prioritize-assumptions/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip prioritize-assumptions.zip -d .agents/skills

Documentation by Pawel Huryn

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

  2. 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

  3. 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

  4. Present results as a prioritized matrix or table.

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

Further Reading

Files

SKILL.md

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

  2. 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

  3. 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

  4. Present results as a prioritized matrix or table.

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

Further Reading

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

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Attribution and licence

Created by
Pawel Huryn
Publisher
Pawel Huryn (Community)
Source
phuryn/pm-skills · pm-product-discovery/skills/prioritize-assumptions · commit 2a12295
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download prioritize-assumptions.zip