Assumption Mapping Expert

Assumption Mapping Expert

Overview

Systematically identify, categorize, and prioritize the assumptions underlying your product decisions. This skill extends Teresa Torres' four risk categories with four additional categories for new products, and uses a devil's advocate approach from PM, Designer, and Engineer perspectives to surface hidden assumptions.

Core Capabilities

• 4-8 category risk model — Value, Usability, Viability, Feasibility (Torres core) plus Ethics, Go-to-Market, Strategy, Team for new products.
• Devil's advocate surfacing — adversarial PM, Designer, and Engineer perspectives expose hidden assumptions.
• Impact x Risk scoring — Risk Score = Impact x (1 - Confidence) ranks what to test first.
• Quadrant classification — Test Now / Proceed / Investigate / Defer with category-matched validation methods.
• Automated tracking — assumption_tracker.py sorts by priority and suggests next actions.

When to Use

• After ideation, before committing to build.
• When a product decision "feels right" but has not been validated.
• When the team disagrees on risk or priority -- assumptions make disagreements explicit.
• Before designing experiments -- test the riskiest assumptions first.

Clarify First

Before mapping assumptions, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• The decision or idea being mapped — the specific product bet whose assumptions you surface (without it the map has no subject)
• Product type — new vs existing (determines whether to use the 4 core categories or the full 8-category model)
• Impact and confidence basis — what evidence sets each impact (1-10) and confidence (high/med/low) (drives Risk Score = Impact × (1 − Confidence) and quadrant placement)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

python3 scripts/assumption_tracker.py --demo            # built-in sample (8 assumptions)
python3 scripts/assumption_tracker.py input.json        # score & prioritize your assumptions
python3 scripts/assumption_tracker.py input.json --format json

Each assumption needs description, category (value/usability/viability/feasibility/ethics/gtm/strategy/team), confidence (high/medium/low), and impact (1-10). Document with assets/assumption_map_template.md.