Individual skill
agentsop-metric-design
Decomposed, multi-criteria metric design for LLM pipelines. The metric IS the model — change the metric and the optimizer changes behavior. Decompose by default; bool during compile, float during eval; calibrate against human; mitigate judge bias. Search keywords: LLM-as-judge, llm as judge, eval metric, evaluation score, scoring function, rubric, RAGAS, G-Eval, judge bias, verbosity bias, how to evaluate LLM output.
skills/agentsop-metric-design/SKILL.md
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- agent-skill Repository topic
- skill-creation Repository topic
- ai-agent Repository topic
- llm-agent Repository topic
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README.mddoc · 2,505 bytesSKILL.mdcore · 25,936 bytesSKILL.mdcore · 25,936 bytesSKILL.mdcore · 25,936 bytesSKILL.mdcore · 25,936 bytesSKILL.mdcore · 25,936 bytesintermediate/operation_candidates.jsonasset · 6,792 bytesreferences/R1-source-evidence.mddoc · 10,311 bytesreferences/R2-judge-bias-catalog.mddoc · 8,118 bytes
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# metric-design — Decomposed, Multi-Criteria Metrics for LLM Pipelines
> "It's unproductive to launch optimization runs using a poorly designed program or a bad metric."
> — DSPy core team [dspy.ai/learn/optimization/overview/]
> "LLM judges exhibit self-preference, recency, rubric-order, score-ID, and length biases."
> — Synthesized from [arxiv.org/pdf/2506.02592, arxiv.org/pdf/2509.26072]
This is a **tool skill**. It produces a metric function (and a calibration receipt) that other skills consume — DSPy compilers (MIPROv2 / GEPA / BootstrapFewShot), LlamaIndex `FaithfulnessEvaluator`/`RelevancyEvaluator`/`RetrieverEvaluator`, LangGraph eval judges, RAGAS, TruLens. The metric is the optimization target. Get it wrong and every downstream optimizer is theatre.
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## 1. 何时激活 (When to Activate)
