「ema」の検索結果

Um sistema híbrido de memória que oferece gerenciamento de conhecimento persistente e pesquisável para agentes de IA (Arquitetura, Padrões, Decisões).

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Especialista em CrewAI - o principal framework multi-agente baseado em papéis utilizado por 60% das empresas da Fortune 500. Abrange design de agentes com papéis e objetivos, definição de tarefas, orquestração de crew, tipos de processo (sequencial, hierárquico, paralelo), sistemas de memória e flows para workflows complexos. Essencial para construir equipes colaborativas de agentes IA. Use quando: crewai, equipe multi-agente, papéis de agentes, crew de agentes, agentes baseados em papéis.

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Plataforma autônoma de agentes de IA para construir e implantar agentes contínuos. Use ao criar agentes de workflow visual, implantar agentes autônomos persistentes ou construir sistemas complexos de automação de IA com múltiplas etapas.

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Framework para construir aplicações com LLM usando agentes, chains e RAG. Suporta múltiplos provedores (OpenAI, Anthropic, Google), 500+ integrações, agentes ReAct, tool calling, gerenciamento de memória e recuperação de vector stores. Use para construir chatbots, sistemas de perguntas e respostas, agentes autônomos ou aplicações RAG. Ideal para prototipagem rápida e deployments em produção.

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Agentes autônomos são sistemas de IA que conseguem decompor objetivos, planejar ações, executar ferramentas e se auto-corrigir sem orientação humana constante. O desafio não é torná-los capazes - é torná-los confiáveis. Cada decisão extra multiplica a probabilidade de falha. Esta habilidade cobre loops de agentes (ReAct, Plan-Execute), decomposição de objetivos, padrões de reflexão e confiabilidade em produção. Insight-chave: taxas de erro compostas matam agentes autônomos. Uma taxa de sucesso de 95% por passo cai para 60% b

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CLI do Datadog para buscar logs, consultar métricas, rastrear requisições e gerenciar dashboards. Use isso ao depurar problemas em produção ou trabalhar com observabilidade do Datadog.

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API de treinamento distribuído mais simples. 4 linhas para adicionar suporte distribuído a qualquer script PyTorch. API unificada para DeepSpeed/FSDP/Megatron/DDP. Posicionamento automático de device, precisão mista (FP16/BF16/FP8). Config interativo, comando de launch único. Padrão do ecossistema HuggingFace.

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Framework PyTorch de alto nível com classe Trainer, treinamento distribuído automático (DDP/FSDP/DeepSpeed), sistema de callbacks e boilerplate mínimo. Escala de laptop para supercomputador com o mesmo código. Use quando quiser loops de treinamento limpos com boas práticas integradas.

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Sistemas de memória persistente para conversas com LLM, incluindo memória de curto prazo, longo prazo e baseada em entidades. Use quando: memória de conversa, lembrar, persistência de memória, memória de longo prazo, histórico de chat.

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Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"

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Create, edit, review, preview, and export polished Marp Markdown slide decks in Canvas Notebook. Use when the user wants Markdown-native presentations, `.marp.md` or `.slides.md` files, version-controlled decks, Marp PDF/image/PPTX/HTML export, speaker notes, dashboard-style slides, data cards, or better looking decks that should remain close to plain text.

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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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Semantic search over Qdrant Cloud collections using the Python client. Covers vector search, filtered search, prefetch+RRF fusion, group API, recommend API, discovery API, batch queries, scroll, and payload indexing. Use when building search features, adding new query types, or working with qdrant-client.

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Use when generating a single dense technical architecture, reference poster, or system diagram image for a software project. Produces a source-grounded bitmap diagram with topology, primitives, ingest/process flow, core logic, state scopes, tiers/modules, surfaces, runtime cards, and strict semantic color rules.

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Plan and document handoffs, parent lane contracts, and parallel batch contracts between specialized AI agents (foreman, workers, reviewers). Use for multi-agent workflows, subagents, original goal preservation, native gates, claim ceilings, terminal states, baton passes, or guild-style agent coordination.

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Use when the user wants the full package on a repo, says run my project workflow, project check-in, or is overwhelmed opening an old or new folder. Runs the project onboarding checklist—explain-new-project, project-worth-my-time, and project-catch-up—in order or on demand.

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Use when the user asks to create, add, convert, publish, or deploy a blog post from a draft, notes, Markdown, outline, or topic. Turns a draft into a complete, SEO/GEO-optimized post wired into the site (template, blog index, sitemap, routing) with QA and deploy. Framework-agnostic — discover the project's own conventions first.

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Triage a GitHub issue that already has the cc-issue-created label and is assumed to be ready to start. First inspect the comment history to decide whether human confirmation is needed (cc-need-human-check, highest priority); otherwise decide whether the issue should be closed as not needed, unanswered confirmation items remain (cc-answer-issue-questions), the description is stale relative to settled comment-history content and must be refreshed before execution (cc-update-issue), or it can move to execution (cc-exec-issue). Dependency checks are out of scope.

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Update an existing GitHub Issue's description based on the issue number. Reads all issue comments and reflects any items not yet captured in the description. After refreshing the description, reviews it for remaining ambiguities or unclear requirements and posts any open questions as a follow-up 確認事項 comment.

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Virtual QA Lead that turns requirements into review-ready, TestRail-importable test cases in one conversation. Use it whenever the user has requirements, a spec, a user story, or acceptance criteria (PDF, DOCX, XLSX, TXT, MD, or pasted text) and wants test cases, a test plan, a checklist, test coverage, a regression suite, or a TestRail/Qase/Zephyr export, even without saying "test cases" ("write tests for this spec", "what should we check here?"). Especially useful when they attach their team's existing test cases to match. When the spec describes an API (endpoints, status codes, schemas, auth, OpenAPI/Swagger), Casely also builds a runnable Postman collection, so use it for "API tests", "Postman collection", or "tests I can run in Newman/CI". Triggers on the same request in any language.

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Crawl websites and extract content from multiple pages via the Tavily CLI. Use this skill when the user wants to crawl a site, download documentation, extract an entire docs section, bulk-extract pages, save a site as local markdown files, or says "crawl", "get all the pages", "download the docs", "extract everything under /docs", "bulk extract", or needs content from many pages on the same domain. Supports depth/breadth control, path filtering, semantic instructions, and saving each page as a local markdown file.

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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Use when the user wants text to sound more human, says writing sounds "too AI" or "too ChatGPT," asks to humanize or rewrite a draft to feel natural, or shares content wanting it to feel authentic and less robotic. Also applies to LinkedIn posts, blog drafts, or emails where the user wants a more genuine voice.

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Use when Invoice-Downloader work involves mailbox batch tests, truth audit, regression validation, P0/P1/P2 decisions, packaged frontend acceptance, or clean reruns.

日本語の概要は準備中です。原文の説明を表示しています。