Assumption Prioritizer

Prioritize assumptions

by phuryn · MIT license · ★ 26,557 on GitHub · updated Sep 14

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.

---
name: prioritize-assumptions
description: "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."
---

## 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.

Overview

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

Manual install (npx, ZIP)

Claude Codenpx skills add phuryn/pm-skills --skill prioritize-assumptions -g -a claude-code

Claude Code (degit)

npx degit phuryn/pm-skills/pm-product-discovery/skills/prioritize-assumptions#main ~/.claude/skills/prioritize-assumptions

Claude.ai / desktopZip the folder and upload it under Settings → Capabilities → Skills.

Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

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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:

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
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
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
Present results as a prioritized matrix or table.

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

1	---
2	name: prioritize-assumptions
3	description: "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."
4	---
5
6	## Prioritize Assumptions
7
8	Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.
9
10	### Context
11
12	You are helping prioritize assumptions for $ARGUMENTS.
13
14	If the user provides files with assumptions or research data, read them first.
15
16	### Domain Context
17
18	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.
19
20	### Instructions
21
22	The user will provide a list of assumptions to prioritize. Apply the following framework:
23
24	1. For each assumption, evaluate two dimensions:
25	- Impact: The value created by validating this assumption AND the number of customers affected (in ICE: Impact = Opportunity Score × # Customers)
26	- Risk: Defined as (1 - Confidence) × Effort
27
28	2. Categorize each assumption using the Impact × Risk matrix:
29	- Low Impact, Low Risk → Defer testing until higher-priority assumptions are addressed
30	- High Impact, Low Risk → Proceed to implementation (low risk, high reward)
31	- Low Impact, High Risk → Reject the idea (not worth the investment)
32	- High Impact, High Risk → Design an experiment to test it
33
34	3. For each assumption requiring testing, suggest an experiment that:
35	- Maximizes validated learning with minimal effort
36	- Measures actual behavior, not opinions
37	- Has a clear success metric and threshold
38
39	4. Present results as a prioritized matrix or table.
40
41	Think step by step. Save as markdown if the output is substantial.
42
43	---
44
45	### Further Reading
46
47	- [Assumption Prioritization Canvas: How to Identify And Test The Right Assumptions](https://www.productcompass.pm/p/assumption-prioritization-canvas)
48	- [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
49