WeRead Reading Persona Analysis

Problem

WeRead shelf, notes, and reading statistics are fragmented signals of personal reading behavior. Users often want to understand why they choose certain books, what they do after finishing them, and how their preferences evolve, but they may lack a repeatable analytical framework. This skill turns those signals into an interpretable reading persona profile rather than a plain book list.

How It Works

It works from WeRead data in three steps:
- Data input: reads the --shelf JSON file and optionally the --notes JSON file; the data must belong to the current user.
- Six-dimension inference: uses metrics such as notes density, literature share, practical book share, domain span, completion and reread rates, and new-book share to map behavior to Cognitive Grower, Emotional Healer, Pragmatic Achiever, Aesthetic Idealist, Curious Explorer, or Rational Architect.
- Report generation: produces an HTML report with persona type, reading distribution, quadrant positioning, core insights, representative books, and a reading statement.

The framework relies on thresholds and evidence, for example high note density suggests deep processing, while high completion paired with practical books suggests applied reading.

Limits

The skill depends on the user's own WeRead data and should not be shared with others; without notes, the inference relies more on shelf structure and becomes less confident. It is useful for personal reading review and templated analysis, not as a substitute for psychological assessment or academic reading research.