Multi-Perspective Behavior Judge
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About this skill
Problem
Behavior reviews often skew when driven by a single value system: rules, empathy, utility, and intent can produce different verdicts. skill-multi-judge makes that disagreement explicit by scoring the same everyday event from five fixed personas, helping engineers and users see where the disagreement comes from instead of receiving a one-word judgment.
How It Works
- Trigger: Provide
judge + a specific personal behavior event, for examplejudge cutting in line to buy goods because of time pressure. - Persona dimensions: Rational rules, empathetic feeling, traditional morality, realistic utility, and idealist tolerance each produce a 0–10 score and a one-sentence rationale.
- Weighted aggregation: Scores are combined using 25%, 25%, 20%, 15%, and 15%, then mapped to bands such as excellent, basically correct, neutral, clearly wrong, or seriously inappropriate.
- Output structure: The response consistently includes individual persona scores, weighted total score, final evaluation, and two actionable improvement suggestions.
Scope and Limits
It is intended for reflecting on everyday personal behavior, motives, consequences, and wording, not for illegal activity, violence, personal attacks, sensitive political issues, or extreme ethical cases; such inputs are rejected. If the event lacks detail, the rationale may remain generic, so adding context, affected parties, frequency, and outcomes improves the result.
Use Cases
- Customer-support QA compares one complaint reply across rules, empathy, and outcome lenses
- Copy editors score apology wording that admits mistakes using 0–10 persona ratings
- Users review everyday choices such as cutting in line and collect two actionable fixes
- ML engineers use the fixed persona rubric to annotate whether conceding a request is appropriate
Best For
- Customer-support QA reviewers checking whether replies balance rules, empathy, and outcomes
- Copy editors scoring apology, refusal, and request wording across multiple stance models
- Product managers reflecting on personal decisions with a fixed persona framework and fixes
- ML engineers designing rubrics or annotating behavior examples in AI conversations
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