weakness-detection

<!-- version: 0.1.0 | outputs: memory/ideas/<slug>/weaknesses.json -->

Skill: weakness-detection

Purpose

Before triggering the pivot engine, understand WHY dimensions are weak. A low distribution score might mean "no viral loop" (fixable) or "fundamentally wrong category for organic growth" (structural). Surface the root cause, not just the symptom.

Input

• Idea slug
• memory/ideas/<slug>/scores.json (required)
• All available dimension files in memory/ideas/<slug>/

Weakness Classification

Root Cause Type	Description	Fix Type
Structural	Inherent to the idea, can't be pivoted away	Drop or major pivot
Situational	Weak due to user's current constraints	Fixable (more time, budget)
Knowledge gap	Weak because data is missing	Run more research
Addressable	Weak but has a clear fix	Targeted pivot

Process

<!-- TODO: Add failure mode library by app category --> <!-- TODO: Define threshold for "weak" (e.g., dimension score < 40) -->

1. Load scores.json, identify dimensions scoring below threshold.
2. For each weak dimension, read the source file to understand why.
3. Classify the root cause (structural / situational / knowledge gap / addressable).
4. Describe the failure mode if the idea proceeded without fixing this weakness.

Output

Write to memory/ideas/<slug>/weaknesses.json:

{
  "weak_dimensions": [
    {
      "dimension": "",
      "score": 0,
      "root_cause_type": "structural | situational | knowledge-gap | addressable",
      "root_cause_description": "",
      "failure_mode": ""
    }
  ],
  "critical_weaknesses": [],
  "addressable_weaknesses": [],
  "overall_weakness_severity": "fatal | major | minor"
}

Notes

<!-- TODO: Define "fatal" vs "major" vs "minor" by combination of weak dimensions -->