Identify Assumptions

Extended 8 Categories (New Products)

For new products, four additional risk categories become critical:

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:

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

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

Phase 5: Suggest Tests

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

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

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

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

Tool Reference

assumption_tracker.py

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

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)