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Prompts Impact Risk Assumption Prioritizer

agent product skill risk: low

Impact Risk Assumption Prioritizer

The prompt asks the model to triage a list of assumptions by scoring Impact and Risk, categorize each into an Impact × Risk matrix with four quadrants, suggest minimal-effort exper…

SKILL 1 file

SKILL.md
---
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.

### 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

- [Assumption Prioritization Canvas: How to Identify And Test The Right Assumptions](https://www.productcompass.pm/p/assumption-prioritization-canvas)
- [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)

INPUTS

$ARGUMENTS REQUIRED

target for prioritization (e.g. product or project name)

REQUIRED CONTEXT

  • list of assumptions to prioritize

OPTIONAL CONTEXT

  • files with assumptions or research data

ROLES & RULES

Role assignments

  • You are helping prioritize assumptions for **$ARGUMENTS**.
  1. If the user provides files with assumptions or research data, read them first.
  2. Think step by step.
  3. Save as markdown if the output is substantial.

EXPECTED OUTPUT

Format
markdown
Schema
table
Constraints
  • present results as prioritized matrix or table
  • suggest experiments for high-impact high-risk assumptions

SUCCESS CRITERIA

  • Triage assumptions using an Impact × Risk matrix
  • Suggest targeted experiments for each assumption
  • Present results as a prioritized matrix or table

CAVEATS

Dependencies
  • Requires user-provided list of assumptions
  • Requires files with assumptions or research data if provided
Missing context
  • Exact output format or template for the matrix/table
  • How $ARGUMENTS placeholder is supplied at runtime
  • Whether numeric scores must be produced or only qualitative categories
Ambiguities
  • Risk definition mixes (1-Confidence)×Effort but earlier text references ICE/RICE formulas without clear mapping.
  • Does not specify desired output length or exact table columns.
  • 'Save as markdown if the output is substantial' leaves 'substantial' undefined.

QUALITY

OVERALL
0.76
CLARITY
0.82
SPECIFICITY
0.68
REUSABILITY
0.85
COMPLETENESS
0.72

IMPROVEMENT SUGGESTIONS

  • Add a short example input list of assumptions and the expected table output.
  • Replace or clarify the Risk formula to match the ICE/RICE references already mentioned.
  • Specify required table columns (e.g., Assumption, Impact, Risk, Category, Experiment, Metric).

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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