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write-judge-prompt

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Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.

skills/write-judge-prompt/SKILL.md

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# Write LLM-as-Judge Prompt

Design a binary Pass/Fail LLM-as-Judge evaluator for one specific failure mode. Each judge checks exactly one thing.

## Prerequisites

- Error analysis is complete. The failure mode is identified.
- You have human-labeled traces for this failure mode (at least 20 Pass and 20 Fail examples).
- A code-based evaluator cannot check this failure mode. Exhaust code-based options before reaching for a judge — many failure modes that seem subjective reduce to keyword checks, regex, or API calls when you understand the domain. Example: detecting whether an AI interviewing coach suggests "general" questions (asking about typical behavior instead of a specific past event) seems to require semantic understanding, but in practice a keyword check for words like "usually," "typical," and "normally" could work quite well.

## The Four Components

Every judge prompt requires exactly four components:
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