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Found 2,037 Skills
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
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.
Organize online information of IPs and conduct multi-dimensional evaluation and scoring. Suitable for assessing the adaptation value of IPs such as novels and scripts, analyzing market potential and innovative attributes
Defines evaluation criteria and scoring methodologies for deliverable assessment
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
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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.
Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"
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.
Evaluate educational chapters from dual student and teacher perspectives. This skill should be used when analyzing chapter quality, identifying content gaps, or planning chapter improvements. Reads all lessons in a chapter directory and provides structured analysis with ratings, gap identification, and prioritized recommendations.