「eval」の検索結果

Simple Preference Optimization para alinhamento de LLMs. Alternativa sem modelo de referência ao DPO com melhor desempenho (+6.4 pontos no AlpacaEval 2.0). Sem modelo de referência necessário, mais eficiente que DPO. Use para alinhamento de preferências quando quer treinamento mais simples e rápido que DPO/PPO.

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Especialista em construção de sistemas de Retrieval-Augmented Generation. Domina modelos de embedding, bancos de dados vetoriais, estratégias de chunking e otimização de recuperação para aplicações LLM. Use quando: construir RAG, busca vetorial, embeddings, busca semântica, recuperação de documentos.

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Banco de dados de embeddings de código aberto para aplicações de IA. Armazene embeddings e metadados, realize buscas vetoriais e full-text, filtre por metadados. API simples com 4 funções. Escala de notebooks para clusters de produção. Use para busca semântica, aplicações RAG ou recuperação de documentos. Melhor para desenvolvimento local e projetos de código aberto.

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Modelo da OpenAI que conecta visão e linguagem. Permite classificação de imagens com zero-shot, correspondência imagem-texto e recuperação cross-modal. Treinado em 400M pares imagem-texto. Use para busca de imagens, moderação de conteúdo ou tarefas visão-linguagem sem fine-tuning. Melhor para compreensão geral de imagens.

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Plataforma de observabilidade de IA de código aberto para rastreamento de LLM, avaliação e monitoramento. Use ao debugar aplicações de LLM com rastreamentos detalhados, executar avaliações em datasets ou monitorar sistemas de IA em produção com insights em tempo real.

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Domine estratégias abrangentes de avaliação para aplicações LLM, desde métricas automatizadas até avaliação humana e testes A/B.

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Plataforma de observabilidade para LLM com rastreamento, avaliação e monitoramento. Use ao depurar aplicações LLM, avaliar saídas de modelos em datasets, monitorar sistemas em produção ou construir pipelines de testes sistemáticos para aplicações de IA.

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Testes e benchmarking de agentes LLM incluindo testes comportamentais, avaliação de capacidades, métricas de confiabilidade e monitoramento em produção—onde até os melhores agentes alcançam menos de 50% em benchmarks do mundo real. Use quando: testes de agentes, avaliação de agentes, benchmark de agentes, confiabilidade de agentes, teste de agentes.

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Load this skill when building any web feature, reviewing architecture decisions, or evaluating JavaScript dependencies. Under no circumstances build features that break completely when JavaScript is unavailable or fails. Absolutely always start with semantic HTML, layer CSS enhancements, and add JavaScript as the final, optional layer. Prioritize resilience and universal access over cutting-edge features.

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[Utilities] Use when initializing or re-evaluating portable project context: project-config, project-reference docs, CLAUDE.md, AGENTS.md, universal agent rules, hookless setup. Triggers on: project init, init project, missing project-config, missing project docs, missing CLAUDE.md, missing AGENTS.md.

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[Code Quality] Use when evaluating review feedback, requesting targeted code-quality review, or verifying completion claims.

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Evaluates candidate profiles against a job description and hiring criteria. Use when the user provides candidate profiles plus a role definition and wants consistent screening, matched requirements, and an interview recommendation.

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Evaluates potential channel, integration, affiliate, or strategic partners. Use when the user provides partner profiles plus partnership goals and wants fit scoring, partnership type, and outreach priority.

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Evaluate UI/flows from cognitive load, error prevention, and accessibility perspectives. Apply when reviewing UX, discussing user confusion, high drop-off, or form usability issues.

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Triage a single GitHub PR by PR number. Check out the PR's branch, detect conflicts with the target branch via `gh pr status` (and label the PR with `cc-resolve-conflict` if any are found), generate and evaluate a fix plan via create-review-fix-plan, then take action (add cc-fix-onetime label if fixes are needed, or merge the PR if it's ready).

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Evaluate, rewrite, and select WeChat Official Account article titles for open rate, depth, reader promise, and account tone. Use when drafting, reviewing, publishing, or revising公众号文章标题, especially when titles may be generic, stale, clickbait, AI-flavored, or misaligned with the article's commercial/editorial intent.

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Discover and list all URLs on a website without extracting content, via the Tavily CLI. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", "list all pages", or "site structure". Faster than crawling — returns URLs only. Essential when you know the site but not the exact page. Combine with extract for targeted content retrieval.

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Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Evaluate authorized wireless networks for handshake, PMKID, rogue AP, and enterprise auth weaknesses.

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Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.

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Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure

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GStack learn method for capturing reusable lessons, triggers, and future retrieval cues after debugging, implementation, or release work.

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Validate and implement Sinclair ZX Spectrum 48K ROM-visible behavior in the EMULATORS repository, including reset and restart vectors, IM1/NMI handling, keyboard scanning and repeat, beeper and tape routines, screen/printer output, channels and streams, BASIC tokenization and execution, expression evaluation, memory movement, system variables, calculator stack, floating-point formats, ROM traps, localized ROM selection, and firmware/hardware timing contracts. Use when Codex reviews or edits ZX Spectrum ROM loading, KERNAL-style traps, keyboard, tape, screen, printer, BASIC files, tokens, system variables, startup, interrupts, or compatibility tests.

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