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Found 2,390 Skills
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.
Evaluate a package before adding it as a dependency. Checks registry metadata, adoption, maintenance, security posture, and license. Use when assessing whether a package is trustworthy, comparing alternatives, or reviewing a new dependency.
LLM prompt testing, evaluation, and CI/CD quality gates using Promptfoo. Invoke when: - Setting up prompt evaluation or regression testing - Integrating LLM testing into CI/CD pipelines - Configuring security testing (red teaming, jailbreaks) - Comparing prompt or model performance - Building evaluation suites for RAG, factuality, or safety Keywords: promptfoo, llm evaluation, prompt testing, red team, CI/CD, regression testing
Valuation analysis for a single stock via Longbridge — current PE / PB / PS / EV-EBITDA snapshot, historical percentile (1–3 years), industry median + relative premium, industry rank. Triggers: "估值贵不贵", "是不是被低估", "PE 历史百分位", "PB 分位", "行业溢价", "行业折价", "X 现在适合买不", "估值水平", "估值貴不貴", "是否被低估", "PE 歷史分位", "行業溢價", "行業折價", "is X expensive", "is X undervalued", "PE percentile", "industry valuation premium", "valuation snapshot".
Automated reproduction of comprehensive model evaluation benchmarks following the Benchmark Suite V3. Auto-activates for model benchmarking, comparison evaluation, or performance testing between AI models.
Use when comparing technology stacks, evaluating frameworks/providers, or assessing TCO, security, and ecosystem health for migration decisions.
Analyze SaaS company valuation compression between funding rounds. Use this skill whenever the user asks about: how much a SaaS company's valuation multiple changed between rounds, why the ARR multiple compressed or expanded, comparing a company's compression to macro benchmarks, or explaining what drove valuation changes for any VC-backed software company. Trigger on phrases like "valuation compression", "ARR multiple", "round-to-round valuation", "multiple change", or when the user asks to compare a company's funding rounds. Always use this skill for any multi-round SaaS valuation analysis — do not try to answer from memory alone.
Systematic framework for evaluating scholarly and research work based on the ScholarEval methodology. This skill should be used when assessing research papers, evaluating literature reviews, scoring research methodologies, analyzing scientific writing quality, or applying structured evaluation criteria to academic work. Provides comprehensive assessment across multiple dimensions including problem formulation, literature review, methodology, data collection, analysis, results interpretation, and scholarly writing quality.
INVOKE THIS SKILL for LLM-as-judge evaluation workflows on Arize: creating/updating evaluators, running evaluations on spans or experiments, tasks, trigger-run, column mapping, and continuous monitoring. Use when the user says: create an evaluator, LLM judge, hallucination/faithfulness/correctness/relevance, run eval, score my spans or experiment, ax tasks, trigger-run, trigger eval, column mapping, continuous monitoring, query filter for evals, evaluator version, or improve an evaluator prompt.
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.