Idea Scoring

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Skill: idea-scoring

Purpose

Produce a single, defensible verdict on an idea by aggregating all available dimension scores using a multiplicative-floor algorithm — a single catastrophic weakness kills the score, just like it kills a real startup. Includes a Riskiest Assumption Test (RAT) to convert the verdict into a concrete next action.

Input

• Idea slug
• One or more of the following (uses whatever is available):
  • memory/ideas/<slug>/idea.md
  • memory/ideas/<slug>/desire_scores.json
  • memory/ideas/<slug>/competitors.json
  • memory/ideas/<slug>/pricing.json
  • memory/ideas/<slug>/cac.json
  • memory/ideas/<slug>/market_size.json
  • memory/ideas/<slug>/distribution.json
  • memory/ideas/<slug>/retention.json
  • memory/ideas/<slug>/complexity.json
  • memory/ideas/<slug>/weighted_signals.json
• Optional: memory/user_profile.md (for founder-market fit)

Minimum Viable Input

At least 3 of 7 dimensions must have source data. If fewer are available, refuse to score and list what's missing. Two dimensions are mandatory — Demand and Distribution. Without evidence of a real problem and a path to reach users, scoring is meaningless.

Scoring Dimensions

Dimension Score Mapping

Each dimension maps source data to a 0–100 sub-score using the rubrics below. When source data uses qualitative labels, apply these conversions.

Demand (0–100)

Adjust within range: +10 if cross-platform resonance confirmed, +5 if monetization_validated is true in idea.md.

Competition (0–100)

Higher = more favorable competitive landscape (counterintuitive — think of it as "opportunity score").

Adjust: +10 if top competitor complaints reveal an unserved pain point. -15 if a FAANG-class player owns the category.

Monetization (0–100)

Adjust: +10 if freemium_conversion_estimate > 5%. +5 if market_size_verdict = "large".

Distribution (0–100)

Adjust: +10 if founder has existing audience or distribution edge (from user_profile.md).

Retention (0–100)

Adjust: +5 if natural_usage_frequency is daily. -10 if weekly-or-less with no external trigger.

Founder-Market Fit (0–100)

If user_profile.md is unavailable, default to 50 (neutral) and flag as missing.

Scoring Algorithm

Step 1 — Compute dimension sub-scores

Apply the mapping rubrics above. Record each as d_i (0–100).

Step 2 — Apply floor penalty (the "Killer Dimension" rule)

Any dimension scoring below 25 is a potential startup killer. Apply this penalty:

floor_penalty = 1.0
for each dimension d_i:
    if d_i < 25:
        floor_penalty *= (d_i / 25)

This multiplicative penalty means a single catastrophic weakness (score 0–10) can halve or destroy the final score, regardless of how strong other dimensions are. This reflects startup reality: brilliant distribution cannot save a product nobody wants.

Step 3 — Compute weighted base score

weights = {
    demand: 0.20,
    competition: 0.10,
    monetization: 0.20,
    distribution: 0.20,
    retention: 0.15,
    founder_market_fit: 0.15
}

base_score = sum(d_i * w_i for each dimension)

Step 4 — Apply floor penalty and missing-input discount

missing_discount = available_dimensions / total_dimensions
adjusted_score = base_score * floor_penalty * missing_discount
final_score = round(clamp(adjusted_score, 0, 100))

The missing-input discount ensures that ideas scored on only 3 of 7 dimensions can never reach the "pursue" tier without completing more analysis.

Step 5 — Determine confidence level

Step 6 — Issue verdict

Riskiest Assumption Test (RAT)

Every idea rests on assumptions. The RAT identifies the single assumption that, if wrong, kills the idea — and designs the cheapest possible experiment to test it before building anything.