identify-assumptions

name	identify-assumptions
description	Assumption mapping expert that identifies, categorizes, and prioritizes product assumptions across 4-8 risk categories using devil's advocate analysis.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"project-management","domain":"product-discovery","updated":"2026-03-04T00:00:00.000Z","python-tools":"assumption_tracker.py","tech-stack":"assumption-mapping, risk-matrix, teresa-torres, continuous-discovery"}

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.

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.

Risk Categories

Core 4 Categories (Existing Products)

These four categories come from Teresa Torres' Continuous Discovery Habits and cover the primary risks for features within an established product:

Category	Question It Answers	Example Assumption
Value	Will customers want this?	"Users will prefer AI-generated summaries over manual note-taking."
Usability	Can customers figure out how to use it?	"Users will understand the drag-and-drop interface without a tutorial."
Viability	Can the business sustain this?	"The feature will generate enough upgrades to justify the engineering cost."
Feasibility	Can we build this?	"Our current infrastructure can handle real-time processing at scale."

Extended 8 Categories (New Products)

For new products, four additional risk categories become critical:

Category	Question It Answers	Example Assumption
Ethics	Should we build this? Are there unintended harms?	"Collecting location data will not create privacy concerns for our target segment."
Go-to-Market	Can we reach and acquire customers?	"Our target segment actively searches for solutions on Google, making SEO viable."
Strategy & Objectives	Does this align with where we want to go?	"Entering the SMB market will not dilute our enterprise positioning."
Team	Do we have the right people and skills?	"Our team can learn the required ML skills within the project timeline."

Methodology

Phase 1: Devil's Advocate Assumption Surfacing

For each product idea or decision, adopt three adversarial perspectives:

PM Devil's Advocate "I challenge whether this is worth building."

• Is there real demand, or are we projecting our own preferences?
• Will this move the metric we care about?
• Can we sustain this economically?
• Does this align with strategy, or is it a distraction?

Designer Devil's Advocate "I challenge whether users will actually use this."

• Will users discover this feature?
• Can they complete the task without help?
• Does this add complexity that hurts the overall experience?
• Are we designing for edge cases and assuming they are common?

Engineer Devil's Advocate "I challenge whether we can build and maintain this."

• Do we have the technical skills and infrastructure?
• What are the hidden dependencies and integration risks?
• Can this scale if it succeeds?
• What is the ongoing maintenance burden?

Phase 2: Categorize Each Assumption

For each assumption surfaced, assign:

Field	Options
Description	Clear, specific statement of what must be true
Risk Category	Value / Usability / Viability / Feasibility / Ethics / Go-to-Market / Strategy / Team
Confidence	High (we have strong evidence) / Medium (some evidence, not conclusive) / Low (gut feeling or no evidence)
Impact	1-10 scale (if this assumption is wrong, how bad is it?)

Phase 3: Prioritize Using Impact x Risk Matrix

Calculate a risk score for each assumption:

Risk Score = Impact x (1 - Confidence)

Where confidence maps to: High = 0.8, Medium = 0.5, Low = 0.2

Impact	Confidence	Risk Score	Meaning
9	Low (0.2)	7.2	Critical -- test immediately
9	High (0.8)	1.8	Important but well-understood
3	Low (0.2)	2.4	Low priority
3	High (0.8)	0.6	Ignore

Phase 4: Classify into Quadrants

Place each assumption on a 2x2 matrix:

                    HIGH IMPACT
                        |
     Proceed with       |     Test Now
     Confidence         |     (highest priority)
                        |
  ──────────────────────┼──────────────────────
                        |
     Defer              |     Investigate
     (low priority)     |     (may be important)
                        |
                    LOW IMPACT

         LOW RISK ◄─────┼─────► HIGH RISK

Quadrant	Impact	Risk	Action
Test Now	High	High	Design an experiment immediately
Proceed	High	Low	Move forward with monitoring
Investigate	Low	High	Gather more data, may upgrade to Test Now
Defer	Low	Low	Accept the risk, revisit if context changes

Phase 5: Suggest Tests

For each "Test Now" assumption, recommend a validation approach:

Assumption Type	Suggested Test Methods
Value assumptions	Customer interviews, fake door test, landing page test
Usability assumptions	Usability test (5 users), prototype walkthrough
Viability assumptions	Financial modeling, pricing experiment, unit economics analysis
Feasibility assumptions	Technical spike, proof of concept, architecture review
Ethics assumptions	Ethics review board, user consent study, regulatory consultation
Go-to-Market assumptions	Channel experiment, SEO keyword test, paid ad test
Strategy assumptions	Strategy review with leadership, competitive analysis
Team assumptions	Skills assessment, hiring timeline analysis, training feasibility

Python Tool: assumption_tracker.py

Track and prioritize assumptions using the CLI tool:

# Run with demo data
python3 scripts/assumption_tracker.py --demo

# Run with custom input
python3 scripts/assumption_tracker.py input.json

# Output as JSON
python3 scripts/assumption_tracker.py input.json --format json

Input Format

{
  "assumptions": [
    {
      "description": "Users will prefer AI summaries over manual notes",
      "category": "value",
      "confidence": "low",
      "impact": 9
    }
  ]
}

Output

Sorted by risk priority with quadrant classification and suggested actions.

See scripts/assumption_tracker.py for full documentation.

Output Format

Assumption Registry

#	Assumption	Category	Confidence	Impact	Risk Score	Quadrant
1	...	Value	Low	9	7.2	Test Now
2	...	Feasibility	Medium	8	4.0	Test Now
3	...	Usability	High	7	1.4	Proceed
4	...	GTM	Low	3	2.4	Investigate

Action Plan for "Test Now" Assumptions

For each assumption in the Test Now quadrant, document:

• Assumption description
• Why it is high risk
• Suggested validation method
• Owner and timeline

Use assets/assumption_map_template.md for the full template.

Integration with Other Discovery Skills

• Use brainstorm-ideas/ to generate ideas whose assumptions you will map.
• Feed "Test Now" assumptions into brainstorm-experiments/ for experiment design.
• Run pre-mortem/ to catch risks that assumption mapping might miss (especially elephants).

Troubleshooting

Symptom	Likely Cause	Resolution
All assumptions classified as "Test Now"	Impact scores inflated or confidence levels consistently set to "low"	Calibrate impact scoring with team; use relative ranking (force distribution across quadrants)
Validation fails with category error	Category string does not match valid set exactly	Use lowercase: value, usability, viability, feasibility, ethics, gtm, strategy, team
Risk scores cluster around the same value	Impact and confidence values lack variance across assumptions	Use the full 1-10 impact scale; challenge the team to differentiate high vs. medium vs. low confidence with evidence
Team generates fewer than 10 assumptions	Devil's Advocate perspectives not applied systematically	Run all three perspectives (PM, Designer, Engineer) independently before combining; use the prompts in Phase 1
"Defer" quadrant is empty	All assumptions scored as high impact, or low-impact assumptions not captured	Include operational and edge-case assumptions; not every assumption is existential
Suggested tests are too generic	Tool uses category-level test suggestions, not assumption-specific ones	Use the category suggestion as a starting point; tailor the test method using brainstorm-experiments/ skill
Assumptions not updated after experiments	No feedback loop from experiment results back to assumption tracker	Re-run assumption_tracker.py with updated confidence levels after each experiment completes

Success Criteria

• All product decisions have at least 8-15 explicit assumptions documented before build commitment
• Every "Test Now" assumption has an assigned owner, validation method, and timeline
• Risk scores are calculated consistently using the Impact x (1 - Confidence) formula
• Assumptions are re-scored after each validation experiment (confidence levels update)
• At least 80% of "Test Now" assumptions are validated or invalidated before major build decisions
• Assumption map is reviewed and updated at every product discovery cycle (weekly or bi-weekly)
• The team can articulate the top 3 riskiest assumptions for any active initiative

Scope & Limitations

In Scope:

• Systematic assumption identification using PM/Designer/Engineer devil's advocate perspectives
• 8-category risk classification (Value, Usability, Viability, Feasibility, Ethics, Go-to-Market, Strategy, Team)
• Quantitative risk scoring using Impact x (1 - Confidence) formula
• Quadrant classification (Test Now, Proceed, Investigate, Defer) with suggested validation methods
• Assumption registry with priority sorting and action plan generation

Out of Scope:

• Running validation experiments (see brainstorm-experiments/ skill)
• Product strategy or roadmap decisions (see execution/outcome-roadmap/)
• Technical feasibility deep-dives or architecture reviews (see engineering/ skills)
• Financial modeling for viability assumptions (see finance/ domain skills)

Important Caveats:

• Confidence levels (high/medium/low) map to fixed numeric values (0.8/0.5/0.2). This is a simplification; real confidence is continuous.
• The quadrant threshold for "high impact" is set at 7/10. Adjust this threshold for your risk tolerance.
• Assumption mapping is most effective when done collaboratively (Product Trio), not by a single person.

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

Tool Reference

assumption_tracker.py

Tracks, scores, and prioritizes product assumptions using an Impact x Risk matrix with quadrant classification.

Flag	Type	Default	Description
input_file	positional	(optional)	Path to JSON file with assumptions array
--demo	flag	off	Run with built-in sample data (8 assumptions across all categories)
--format	choice	text	Output format: text or json

Input fields per assumption:

• description (required): Clear statement of what must be true
• category (required): One of value, usability, viability, feasibility, ethics, gtm, strategy, team
• confidence (required): One of high, medium, low
• impact (required): Integer 1-10

References

• Teresa Torres, Continuous Discovery Habits (2021)
• David J. Bland & Alexander Osterwalder, Testing Business Ideas (2019)
• Ash Maurya, Running Lean (2012)
• Marty Cagan, Inspired (2018)