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, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• 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).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

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.

Anti-Patterns

Everything rated risky

Mistake: The map lists dozens of assumptions, nearly all high impact and low confidence, so nothing can be tested in order. Why it happens: Rating an assumption low-risk feels like tempting fate. Instead: Keep no more than about 20% of assumptions in the Test Now quadrant. If more land there, re-score impact and confidence against real evidence until the load-bearing ones stand out.

Beliefs marked as facts

Mistake: "Users want a faster checkout" is rated high confidence because "we know this," with no evidence cited. Why it happens: The most dangerous assumptions feel obviously true. Instead: Cite the evidence for every high confidence rating. "We know this" is not evidence. Without a source, rate it low and let assumption_tracker.py rank it.

Testing the easiest assumption first

Mistake: The team tests the assumption with the easiest experiment, not the one the idea depends on most. Why it happens: Hard experiments are uncomfortable, and easy ones give quick wins. Instead: Rank by Risk Score = Impact x (1 - Confidence) and test the top three first, even when the experiment is hard. Give each one an owner and a date.

Nine more, each with a bad and a good example: references/red-flags.md.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/methodology-and-tools.md — the full 4-8 category tables with examples, the devil's advocate prompts, the 5-phase scoring/quadrant process, assumption_tracker.py usage and flags, output formats, troubleshooting, success criteria, and bibliography. Read when mapping or scripting assumptions.
• references/assumption-mapping-guide.md — deep theory: Torres' four risks and the extended 8-category model with red flags per category, confidence calibration techniques (evidence-based, Five Whys, pre-mortem check), the prioritization matrix with tripwires, and assumption-to-experiment mapping. Read for the underlying framework.
• references/red-flags.md — anti-patterns (assumption inflation, miscategorization, confidence without evidence) with bad/good examples anchored in Torres' categories. Read before sharing an assumption map.

Scope & Limitations

In Scope: systematic assumption identification using PM/Designer/Engineer devil's advocate perspectives; 8-category risk classification; quantitative scoring with Impact x (1 - Confidence); quadrant classification with suggested validation methods; assumption registry with priority sorting and action plans.

Out of Scope: running validation experiments (brainstorm-experiments/); product strategy or roadmap decisions (execution/outcome-roadmap/); technical feasibility deep-dives (engineering/ skills); financial modeling for viability (finance/ skills).

Important Caveats: confidence levels map to fixed numeric values (0.8/0.5/0.2) — a simplification of continuous confidence; the "high impact" threshold is 7/10, adjustable for your risk tolerance; assumption mapping works best collaboratively (Product Trio), not solo.

Integration Points

| Integration | Direction | Description | |------------|-----------|-------------| | brainstorm-ideas/ | Receives from | Ideas generated become the subjects whose assumptions are mapped | | brainstorm-experiments/ | Feeds into | "Test Now" assumptions become hypotheses for experiment design | | pre-mortem/ | Complements | Pre-mortem catches risks that assumption mapping may miss (especially elephants) | | execution/create-prd/ | Feeds into | Validated assumptions populate the PRD Assumptions section (Section 7) | | execution/brainstorm-okrs/ | Feeds into | Viability assumptions inform OKR key result selection and confidence levels | | senior-pm/ | Feeds into | High-impact assumptions feed into portfolio risk registers |