“LLM evaluation”

Use this skill when you need to design LLM evaluation datasets, judges, metrics, and human-review boundaries; triggers include LLM Eval 设计 and llm evaluation…

Use this skill when you need to design LLM evaluation datasets, judges, metrics, and human-review boundaries; triggers include llm evaluation design.

This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias…

ML model evaluation and experiment tracking -- LLM benchmarking with lm-evaluation-harness (MMLU, GSM8K, HumanEval, 60+ tasks), experiment tracking and…

Use when designing or auditing how an LLM or multimodal LLM is evaluated on a clinical task (report generation, VQA, text extraction).

Prompt engineering and LLM evaluation.

End-to-end LLM accuracy evaluation on AMD ROCm (ROCm-only) — container setup, vLLM/SGLang/ATOM serving, lm-eval / lighteval / evalscope benchmarks.

End-to-end LLM accuracy evaluation on AMD ROCm (ROCm-only) — container setup, vLLM/SGLang/ATOM serving, lm-eval / lighteval / evalscope benchmarks.

End-to-end LLM accuracy evaluation on AMD ROCm (ROCm-only) — docker container setup OR host (no-docker) runtime, vLLM/SGLang/ATOM serving, lm-eval / lighteval…

This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates…

Design and evaluate LLM product features, retrieval, validated outputs, latency, and cost controls.

Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation.

Author continuously-running online evaluations in PostHog AI observability, grounded in real failure modes you've identified.

Use when validating, benchmarking, or monitoring LLM application performance.

Build evaluation frameworks for agent systems.

A.I.G Scanner — AI security scanning for infrastructure, AI tools / skills, AI Agents, and LLM jailbreak evaluation via Tencent Zhuque Lab AI-Infra-Guard.

Evaluation — an observability concept covering evaluation, score, drift, shadow, benchmark.

Renamed to /model-assessment in v6 (removed in v7).

Use when designing, configuring, running, or troubleshooting NeMo Agent Toolkit evaluations, datasets, evaluator selection, ATIF surfaces, quality gates…

Evaluate one design or a user-approved maturity-mapped batch through a transparent evidence-based rubric.

Use when the user wants proof that finished work meets its acceptance criteria, not just that tests pass: 'verify it is actually done', 'check it against the…

Open-source AI observability platform for LLM tracing, evaluation, and monitoring.

Public-safe preliminary screen for challenges to the impartiality of an AI evaluator, LLM-as-a-judge, decision-support agent, or adjudicative system based on…

LLM observability platform for tracing, evaluation, and monitoring.