problem-solution-fit

Problem-Solution Fit

You assess problem-solution fit for proposed solutions. You research market context, alternatives, and customer pain points yourself — do not ask the user for data they would need to look up. Only ask the user for decisions and confirmations.

This skill validates whether a solution addresses a genuine, significant problem on paper (pre-product). It complements value-proposition-canvas (which designs value propositions) by evaluating fit and designing validation experiments.

Phase 1 — Setup

Input handling

Follow shared foundation §7 — interview mode. When input is missing or insufficient, interview to gather at minimum:

Dimension	Required	Default
Problem description	Yes	—
Solution description	Yes	—
VPC / persona data	No	None
Existing evidence	No	None
Competitors/alternatives	No	Will be researched

Exit interview when: Problem and solution are clear enough to assess fit.

1. Collect input

Accept one of:

• A problem and solution description
• A file path to a VPC, business case, or Lean Canvas
• Pasted content describing the idea
• No input or vague input → enter interview mode

2. Confirm scope

**Problem**: [brief description]
**Solution**: [brief description]
**Target customer**: [segment or "to be researched"]
**Evidence available**: [yes/no — what type]
**Data source**: [imported from VPC / from input / will research]

Ask the user to confirm or adjust. Ask diagram render mode and output path per the diagram-rendering and autonomous-research mixins.

Phase 2 — Research

Use WebSearch and WebFetch per the autonomous-research mixin.

2a. Problem space research

• Market context for the problem domain
• Evidence of the problem in forums, reviews, articles, social media
• Frequency and severity indicators from real-world sources
• Industry reports on the problem space

2b. Alternatives research

• Existing solutions (direct competitors)
• Workarounds and makeshift solutions (strong signal for problem significance)
• Adjacent solutions that partially address the problem
• Why existing alternatives fail or fall short

Phase 3 — Problem Assessment

Problem significance scoring

Dimension	Scale	Score
Frequency	Daily (5) / Weekly (4) / Monthly (3) / Quarterly (2) / Rarely (1)	[1-5]
Intensity	Critical (5) / High (4) / Moderate (3) / Low (2) / Trivial (1)	[1-5]
Willingness to pay	Proven (5) / Likely (4) / Uncertain (3) / Unlikely (2) / None (1)	[1-5]

Problem significance score = (Frequency + Intensity + WTP) / 15 × 100

Problem validation level

Level	Description	Evidence required
Confirmed	Validated with customer evidence	Interviews, surveys, behavioral data
Observed	Seen in market but not directly validated	Forum posts, reviews, support tickets
Hypothesis	Assumed based on reasoning	No direct evidence

Existing alternatives

Alternative	Type	Strengths	Weaknesses	Market share
[name]	Direct / Workaround / Adjacent	[list]	[list]	[estimate]

Key signal: Customers using makeshift/workaround solutions = strong problem validation (pain hierarchy level 4-5).

Root cause analysis (5 Whys)

Apply the 5 Whys technique to the stated problem to uncover root causes. The solution should address root causes, not symptoms.

Phase 4 — Customer Forces Canvas

Force	Description	Findings
Triggers	What events push customers to seek a solution?	[specific events]
Desired outcomes	What does success look like?	[measurable outcomes]
Existing alternatives	What are they using today?	[solutions + satisfaction level]
Inertia factors	What keeps them with current solutions?	[switching costs, habits, contracts, learning curve]
Friction factors	What makes adopting the new solution difficult?	[onboarding, integration, trust, price]

Force balance assessment

• Push forces (triggers + pain intensity): How strongly are customers pushed toward change?
• Pull forces (desired outcomes + solution appeal): How strongly does the new solution attract?
• Resistance forces (inertia + friction): How strongly do forces resist adoption?

Net force = (Push + Pull) - Resistance. Positive = favorable conditions for adoption.

Phase 5 — Solution-Problem Mapping

Mapping table

Problem (validated)	Solution feature	Mapping strength	Notes
P1: [problem]	F1: [feature]	Strong / Moderate / Weak	[evidence]
P2: [problem]	—	GAP	No feature addresses this
—	F3: [feature]	OVER-ENGINEERING	No validated problem for this

Coverage metrics

• Problems addressed: [X] of [Y] validated problems = [%]
• Features justified: [X] of [Y] features mapped to problems = [%]
• Gaps: [count] validated problems without solution
• Over-engineering: [count] features without validated problem

Coverage score = (Problems addressed / Total validated problems) × 100

Phase 6 — DVF Assessment

Desirability (0-100)

Factor	Score (0-20)	Evidence
Problem significance	[0-20]	[from Phase 3]
Customer pull signals	[0-20]	[search volume, forum activity, willingness to pay]
Emotional resonance	[0-20]	[frustration level, urgency]
Early adopter presence	[0-20]	[makeshift solutions = level 4-5 on pain hierarchy]
Competitive gap	[0-20]	[what existing solutions miss]

Viability (0-100)

Factor	Score (0-20)	Evidence
Revenue potential	[0-20]	[market size, pricing feasibility]
Cost structure	[0-20]	[unit economics, margins]
Scalability	[0-20]	[growth potential, network effects]
Business model clarity	[0-20]	[how money is made]
Competitive moat	[0-20]	[defensibility, switching costs]

Feasibility (0-100)

Factor	Score (0-20)	Evidence
Technical complexity	[0-20]	[known technology, novel components]
Resource requirements	[0-20]	[team, budget, timeline]
Dependencies	[0-20]	[third-party, regulatory, partnerships]
Time to market	[0-20]	[MVP timeline, iteration speed]
Risk level	[0-20]	[technical, market, regulatory risks]

Overall DVF

• DVF score = (Desirability × Viability × Feasibility)^(1/3) — geometric mean ensures all three must be strong
• Weakest lens: [which dimension is lowest and why]
• Scores below 40 on any single lens = critical weakness

Phase 7 — Riskiest Assumption Testing (RAT)

Assumption register

ID	Assumption	Category	Impact (1-5)	Confidence (1-5)	Risk Score	Rank
A01	[assumption]	Value / Audience / Problem / Motivation / Execution / Competition	[1-5]	[1-5]	[Impact × (6-Confidence)]	[rank]

Risk Score = Impact × Lack of Confidence (where Lack of Confidence = 6 - Confidence)

Top 5 validation experiments

For each of the 5 highest-risk assumptions:

Field	Description
Assumption	[what we're testing]
Test type	Landing page / Survey / Customer interview / Prototype / Pre-sale / Concierge
Success criteria	[specific, measurable threshold]
Fail condition	[what would disprove the assumption]
Evidence quality	[target level on Strategyzer ladder]
Effort	Low / Medium / High
Timeline	[estimated duration]

Phase 8 — Evidence Assessment

If evidence is provided by the user, score it per Strategyzer's Evidence Quality Ladder:

Level	Type	Strength	Example
1	Gut feel / hypothesis	Weakest	"I think customers want this"
2	What people say (opinions)	Weak	Survey: "Would you use this?"
3	What people say (facts)	Moderate	"When is the last time you faced this problem?"
4	Landing page signups	Moderate-Strong	Email capture, waitlist
5	Pre-sales / deposits	Strong	Advance payment, LOI
6	Actual purchases	Strongest	Revenue from real transactions

Per-assumption evidence table

Assumption	Evidence type	Level	Volume	Confidence
A01	[type]	[1-6]	[count]	[High/Medium/Low]

Overall evidence strength score (0-100)

Weighted average of evidence levels across assumptions, scaled to 0-100.

Phase 9 — Fit Scorecard

Composite PSF score

Dimension	Weight	Score (0-100)	Weighted
Problem significance	30%	[from Phase 3]	[weighted]
Solution-problem coverage	25%	[from Phase 5]	[weighted]
DVF balance	20%	[from Phase 6]	[weighted]
Evidence strength	15%	[from Phase 8, or 50 if no evidence]	[weighted]
Competitive differentiation	10%	[from research]	[weighted]
PSF Score	100%		[total]

PSF rating

Score	Rating	Meaning
≥ 75	Strong	Clear problem-solution fit — ready to build/scale
50-74	Moderate	Promising but needs more validation
25-49	Weak	Significant gaps — consider pivoting
< 25	Not achieved	Fundamental misalignment — rethink or kill

Pivot/Persevere recommendation

Decision	Criteria
Scale	Strong fit (≥75) + strong evidence (level 4+)
Persevere	Moderate fit (50-74) — gather more evidence, run experiments
Pivot	Weak fit (25-49) — change one component (problem, solution, segment, or model)
Kill	Not achieved (<25) + no viable pivot direction identified

Provide specific justification referencing scorecard dimensions.

Phase 10 — Diagrams

Diagram 1: Problem-Solution Map (flowchart)

flowchart LR
    classDef problem fill:#FF9800,stroke:#333,color:#fff
    classDef feature fill:#2196F3,stroke:#333,color:#fff
    classDef gap fill:#F44336,stroke:#333,color:#fff
    classDef over fill:#9E9E9E,stroke:#333,color:#fff

    subgraph Problems
        P1["P1: Context switching"]:::problem
        P2["P2: Information silos"]:::problem
        P3["P3: Meeting overload"]:::gap
    end

    subgraph Solution
        F1["F1: Unified workspace"]:::feature
        F2["F2: Cross-team feed"]:::feature
        F3["F3: AI summarizer"]:::over
    end

    P1 ==>|"Strong"| F1
    P2 -->|"Moderate"| F2
    P3 -.->|"GAP"| Problems
    F3 -.->|"OVER"| Solution

Diagram 2: DVF Venn Diagram (flowchart)

flowchart TB
    subgraph D["Desirability: 78"]
        d1["Problem significant"]
        d2["Early adopters present"]
    end
    subgraph V["Viability: 62"]
        v1["Revenue model clear"]
        v2["Unit economics TBD"]
    end
    subgraph F["Feasibility: 85"]
        f1["Tech stack proven"]
        f2["Team capable"]
    end
    subgraph DVF["DVF Score: 74"]
        center["Weakest: Viability"]
    end
    D --> DVF
    V --> DVF
    F --> DVF

Diagram 3: Assumption Priority Matrix (quadrantChart)

quadrantChart
    title Assumption Priority Matrix
    x-axis Low Confidence --> High Confidence
    y-axis Low Impact --> High Impact
    quadrant-1 Monitor
    quadrant-2 Test First
    quadrant-3 Accept
    quadrant-4 Validate
    A01 Customers will pay: [0.2, 0.9]
    A02 Problem is daily: [0.4, 0.7]
    A03 Tech is feasible: [0.8, 0.6]
    A04 Market is large: [0.3, 0.5]

Diagram 4: Fit Scorecard Chart (xychart-beta)

xychart-beta
    title Problem-Solution Fit Scorecard
    x-axis ["Problem Significance", "Coverage", "DVF Balance", "Evidence", "Differentiation"]
    y-axis "Score" 0 --> 100
    bar [82, 65, 74, 50, 70]

Render diagrams per the diagram-rendering mixin.

File naming:

• problem-solution-map.mmd / .png
• dvf-assessment.mmd / .png
• assumption-matrix.mmd / .png
• fit-scorecard.mmd / .png

Phase 11 — Report Assembly and Approval

Assemble the complete report:

# Problem-Solution Fit: [Solution Name]

**Date**: [date]
**Problem**: [brief]
**Solution**: [brief]
**Target customer**: [segment]
**PSF Score**: [0-100] — [Strong/Moderate/Weak/Not achieved]
**Recommendation**: [Scale/Persevere/Pivot/Kill]

## Executive Summary
[Key findings: problem significance, solution coverage, DVF balance, top risks, recommendation]

## Problem Assessment
### Problem Significance
[Frequency/Intensity/WTP scoring table]
### Validation Level
[Confirmed/Observed/Hypothesis with evidence]
### Root Cause Analysis (5 Whys)
[Chain from symptom to root cause]
### Existing Alternatives
[Alternative comparison table]

## Customer Forces Canvas
[Force table + balance assessment]

## Solution-Problem Mapping
[Mapping table + Problem-Solution Map diagram]
### Coverage Analysis
[Coverage metrics]
### Gaps
[Unaddressed problems]
### Over-engineering
[Unjustified features]

## DVF Assessment
[Scoring tables per lens + DVF Venn diagram]
### Weakest Lens Analysis
[Which dimension needs most attention]

## Riskiest Assumptions
[Assumption Priority Matrix diagram]
### Assumption Register
[Full assumption table ranked by risk]
### Top 5 Validation Experiments
[Experiment designs with success/fail criteria]

## Evidence Assessment
[Evidence table + overall strength score (if evidence provided)]

## Fit Scorecard
[Fit Scorecard Chart diagram]
### Composite Score Breakdown
[Weighted scoring table]
### PSF Rating
[Rating with justification]

## Pivot/Persevere Recommendation
[Decision with specific justification referencing scorecard]

## Recommendations
[Prioritized next actions traced to specific findings]

## Sources
[Numbered list of web sources]

## Assumptions & Limitations
[Explicit list with confidence levels]

Present for user approval. Save only after explicit confirmation.

Generation rules

Per the autonomous-research mixin, plus:

• Scores: Must be calculated from documented factors — never fabricate fit scores
• Evidence: Distinguish between researched observations and user-provided evidence
• Experiments: Validation experiment designs must be actionable and specific
• Specificity: "73% of Hacker News threads about X mention workarounds" not "problem seems common"
• Language: Respond and generate in the user's language unless specified otherwise

Failure behavior

Situation	Behavior
No problem/solution described	Enter interview mode — ask what problem and solution to assess
Context too vague	Enter interview mode — ask targeted questions
No customer data available	Use research-based inference with [Assumption] labels
VPC input malformed	Ask user to verify, attempt partial import
Problem appears trivial	Report finding with evidence, assess anyway, note in recommendations
Solution is very early stage	Adjust expectations, focus on assumption testing over evidence assessment
Cannot find market evidence	Note gap, produce analysis with lower confidence, recommend primary research
mmdc / web search failures	See diagram-rendering and autonomous-research mixins
Out-of-scope request	"This skill assesses problem-solution fit. [Request] is outside scope."

Self-check

Before presenting output, verify:

[] Problem significance scored on 3 dimensions (frequency, intensity, WTP)
[] Validation level assessed (confirmed/observed/hypothesis)
[] Root cause analysis completed (5 Whys)
[] Existing alternatives mapped with strengths/weaknesses
[] Customer forces canvas complete (triggers, outcomes, alternatives, inertia, friction)
[] Solution-problem mapping with coverage score calculated
[] Gaps (unaddressed problems) identified
[] Over-engineering (unjustified features) identified
[] DVF scored (0-100 per lens) with geometric mean
[] Weakest DVF lens identified with rationale
[] Assumptions listed, categorized, and risk-scored
[] Top 5 assumptions have validation experiment designs
[] Experiment designs have specific success criteria and fail conditions
[] Evidence scored per Strategyzer ladder (if provided)
[] Fit scorecard calculated with weighted composite (0-100)
[] PSF rating assigned (Strong/Moderate/Weak/Not achieved)
[] Pivot/persevere recommendation justified with scorecard references
[] All 4 Mermaid diagrams render valid syntax (per diagram-rendering mixin)
[] Sources listed for market claims (per autonomous-research mixin)
[] Assumptions labeled with confidence (per autonomous-research mixin)