What problem does it solve?
When facing a confusing problem, people often stop at surface topics or loose analogies without uncovering the underlying mechanism. This Skill finds the recurring motif beneath a problem, distills the causal structure that generates it, and internally stress-tests that structure against changed conditions so the conclusion states both a transferable rule and its boundaries.
Core Features & Use Cases
- Motif Extraction: Strips domain-specific names from a problem and rewrites it as a plain-language relational question that survives cross-domain substitution.
- Structure Cards with ASCII Diagrams: Produces one or two structure cards covering concept, relation, and example mapping, plus a pure-ASCII relation diagram when multiple structures interact (serial, parallel, nested, checks-and-balances, or feedback).
- Internal Wind-Tunnel Calibration: Changes one key condition on the same example to find where the model breaks, then delivers a two-part conclusion (concrete stress test, then abstract rule) with evidence boundaries stated once.
- Use Case: Ask why team discussions always get anchored by the first speaker; the Skill identifies the motif of early information gaining interpretive authority, maps the causal structure, and shows how the prediction changes when speaking order or information source changes.
Quick Start
Ask the AI to find the structure behind a problem you describe, for example by saying "find the structure behind why our meetings always get anchored by the first opinion".
