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Found 1,942 Skills
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).
Comprehensive framework for evaluating AI vendors and solutions to avoid costly mistakes. Use this skill when assessing AI vendor proposals, conducting due diligence, evaluating contracts, comparing vendors, or making build-vs-buy decisions. Helps identify red flags, assess pricing models, evaluate technical capabilities, and conduct structured vendor comparisons.
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.
Alibaba Cloud Governance Center evaluation report skill. Use for querying governance maturity check results, generating structured risk reports, and account compliance analysis. Triggers: "云治理", "成熟度检测", "合规检查", "安全风险", "治理检测", "governance evaluation", "maturity check", "compliance report", "risk report", "governance center".
Evaluate options for a specific design decision node and recommend one with explicit trade-offs. Use when the design already exposes a concrete choice such as architecture style, state management approach, auth model, storage pattern, sync strategy, multi-agent coordination model, language or runtime, UI framework, data-layer library, or tooling selection. Trigger when the user needs structured comparison and recommendation for a bounded design decision. Do not use for broad design discovery, full-system decomposition, or final readiness review.
Evaluates ML models for performance, fairness, and reliability. Use for metric selection, cross-validation strategies, overfitting/underfitting diagnosis, hyperparameter tuning, LLM evaluation, A/B testing, and production monitoring for model drift.
Make an evidence-based hiring decision and produce a Candidate Evaluation Decision Pack (criteria + scorecard, signal log, work sample/trial plan + rubric, reference check script + summary, decision memo). Use for candidate evaluation, hiring decisions, reference checks, work samples/take-homes, and hiring bar calibration. Category: Hiring & Teams.
Evaluate GitHub contributors for MLOps/engineering roles. Use when analyzing candidates, researching GitHub profiles, or updating CONTRIBUTORS.md with hiring assessments.
Use when testing Ralph's hat collection presets, validating preset configurations, or auditing the preset library for bugs and UX issues.
Strictly and meticulously judge and score story texts, analyze quality from the dimensions of market potential, innovation attributes, and content highlights. Suitable for initial novel screening and multi-dimensional evaluation and scoring
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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.