Stg Scoring Problems

Problem Scoring

Score problem candidates using four-property framework with compression-model elimination. Every score carries a tier label and cited evidence. Output is a problem hypothesis, not a canvas section.

Procedure

Step 1: Load Context [S]

Read: segment hypothesis (or candidates if segment not yet finalized), governor's problem space description, research signals from BUILD phase 1.

Produce: problem generation parameters anchored to segment.

Gate: context_loaded: bool -- segment context and problem space available.

• Pass: Step 2.
• Fail: Report missing inputs. If no segment data, proceed with governor input alone, noting reduced confidence.

Step 2: Enumerate Candidate Problems (Minimum 5) [K-grounded]

Grounded in: segment context, governor's problem description, research signals.

For the target segment, identify 5-7 candidate problems from:

• Governor input (what they believe the problem is)
• Public signals (forums, reviews, job postings, support tickets)
• Competitive analysis (what alternatives solve -- implies the problem)
• Adjacent segment patterns (problems common in related segments)

WebSearch for segment-specific pain signals.

Produce: candidate problem list.

Gate: candidates_enumerated: bool -- at least 5 candidates identified from at least 2 different sources.

• Pass: Step 3.
• Fail: If fewer than 5, broaden search to adjacent problem categories. If still <5, document the search strategy used and proceed with available candidates.

Step 3: Score Each Problem on Four Properties [R]

For each problem, score:

Frequency (1-5):

Severity (1-5):

Breadth (1-5):

Alternatives' Inadequacy (1-5):

Composite = Frequency x Severity x Breadth x Alternatives_Inadequacy (max 625, useful for ranking, not absolute).

Produce: scored problem matrix with per-property tier labels.

Gate: problems_scored: bool -- every candidate scored on all four properties, each score cites evidence and carries tier label.

• Pass: Step 4.
• Fail: Scores without evidence are invalid. WebSearch for missing evidence or assign score 1 with "no evidence found" note.

Step 4: Eliminate Weak Candidates (Compression) [R]

Eliminate problems that score 1-2 on 2+ properties. Record elimination rationale.

If governor's stated problem is eliminated, flag explicitly -- do not silently drop it. Report: "Your stated problem scored [X] but was eliminated because [rationale]. Evidence for alternative [Y] is stronger because [evidence]."

Produce: surviving problems + elimination log.

Gate: compression_complete: bool -- elimination rationale documented, governor's problem status explicitly stated.

• Pass: Step 5.
• Fail: If all problems eliminated, broaden candidate generation. If governor's problem is the only survivor despite weak evidence, flag potential confirmation bias.

Step 5: Write Hypothesis [S]

Primary = highest composite score. Write in register format:

• Claim: State the problem independent of any solution. "Customers struggle with X" not "customers need a tool that does Y." One paragraph.
• Evidence: Tier-labeled evidence items from scoring (per-property evidence).
• Possibility Space: All candidates considered, eliminated candidates with rationale, alternatives carried.
• Assumptions: What must be true for this problem to be worth solving (each with tier, load-bearing flag, blast radius).
• Kill Condition: What would prove this problem is not worth solving. Must reference specific observable thresholds. Example: "Interviews reveal <2/5 people in the segment experience this problem weekly" or "existing alternative achieves >80% satisfaction."

Produce: complete problem hypothesis in register format.

Gate: hypothesis_written: bool -- claim is solution-independent, kill condition references observable thresholds, possibility space records all candidates.

• Pass: Done.
• Fail: If claim contains solution language, rewrite as pain statement. If kill condition is vague, add specific thresholds.

Quality Criteria

• Minimum 5 candidate problems enumerated
• Each problem scored on all four properties with tier labels
• Every score cites specific evidence (not "likely high frequency")
• Governor's stated problem is evaluated honestly (not auto-promoted)
• Composite score used for ranking, not as false-precision metric
• Kill condition references specific observable thresholds
• Problem statement is independent of solution ("customers struggle with X" not "customers need a tool that does Y")

Failure Modes

Boundaries

In scope: Problem enumeration, four-property scoring with evidence, compression-model elimination, possibility space recording, hypothesis writing in register format.

Out of scope: Market sizing (stg-sizing-markets), segment definition (stg-segmenting-customers), competitive mapping (stg-analyzing-competition), solution design (stg-designing-solutions).