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概要と使いどころ
Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.
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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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Orientação especializada para treinamento com Fully Sharded Data Parallel do PyTorch (FSDP) - sharding de parâmetros, precisão mista, CPU offloading, FSDP2
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Framework de dados para construir aplicações LLM com RAG. Especializado em ingestão de documentos (300+ conectores), indexação e consultas. Inclui índices vetoriais, mecanismos de consulta, agentes e suporte multimodal. Use para perguntas sobre documentos, chatbots, recuperação de conhecimento ou construção de pipelines RAG. Ideal para aplicações LLM centradas em dados.
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Framework de orquestração multi-agent para colaboração autônoma de IA. Use ao construir equipes de agentes especializados trabalhando juntos em tarefas complexas, quando você precisa de colaboração de agentes baseada em papéis com memória, ou para workflows em produção exigindo execução sequencial/hierárquica. Construído sem dependências de LangChain para execução enxuta e rápida.
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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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Crie e gerencie arquivos de planejamento markdown persistentes para execução estruturada de tarefas. Use quando o usuário pedir para "criar um plano", "acompanhar progresso", "iniciar um projeto de pesquisa", ou quando uma tarefa exigir mais de 5 chamadas de ferramenta e precisar de rastreamento de fases estruturado para manter o foco e evitar desvio de objetivo.
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[Implementation] Use when you need to start coding & testing an existing plan. Flags: --approval=off (auto/trust mode, no approval gate), --tests=off (skip the test step), --parallel={auto|on|off} (default off — sequential; --parallel/=on opts in to parallel sub-agent waves; =auto fans out only when the plan declares PAR/SEQ tags and write sets).
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给定 PMID/PMCID 取结构化全文（NLM BioC XML/JSON）。封装 BioC PubMed（标题+摘要）和 BioC PMC OA（开放获取全文）端点，输出按章节切分的段落、表格上下文、RAG 友好 chunk，适合大批量入向量库。Fetches structured fulltext from NLM BioC API for PMID/PMCID, outputs section-segmented paragraphs and RAG-ready chunks. 触发词：全文抽取 / BioC / RAG 入库 / 文献全文 / PMC 开放获取 / structured fulltext / 段落抽取 / 表格抽取 / chunking / fulltext to vector store / paragraph extraction.
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Implement or refactor Laravel controllers, commands, jobs, listeners, and scheduled tasks. Use for endpoints, authentication, request validation, simple reads, or extracting business orchestration into Actions.
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값 반환과 부수효과가 혼재된 메서드를 Query(순수)와 Modifier(부수효과)로 분리(CQS). "쿼리와 명령 분리", "getter가 상태를 바꿔", "CQS", "/separate-query-modifier" 요청 시 사용. 단, I/O 전체를 계산에서 떼어내는 것은 /segregate-functional-core가 적합. /separate-query-modifier [commit-ref]로 호출.
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반복되는 파라미터 그룹을 객체로 치환(IPO)하거나, 객체에서 꺼낸 값 대신 객체 자체를 전달(Preserve Whole Object). "파라미터 객체", "인자가 너무 많아", "start·end 대신 Range로", "/introduce-parameter-object" 요청 시 사용. 단, 결과 객체가 검증·연산을 가진 도메인 값이면 /discover-value-object가 적합. /introduce-parameter-object [commit-ref]로 호출.
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Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.
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Knowledge base from FAA-STD-025f (Preparation of Interface Documentation). Use for IRD/ICD family definitions, common format and content, IRD shall-requirements, ICD design characteristics, verification/VRTM, and CM/revision/approval of FAA Enterprise Subsystem interfaces. Covers rev F preparation rules only; does not replace FAA-STD-005 drawing/spec prep, Order 1800.66 full CM procedures, or non-FAA interface standards.
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Drafts the instructions field of a declarative agent that orchestrates a set of custom skills, under a character cap (default 8,000), from the skills' names and descriptions: role and scope, one routing rule per skill, workflow order, gates written as workflow holds, global rules, output format and what the agent never does. Returns the text in a fenced block with its character count labelled counted or estimated, plus a skill inventory, routing table, gate table, rules trace and open questions for the owner. Use when the user asks to "write the instructions for my agent", "draft a system prompt that ties these skills together", "tighten these agent instructions to fit the cap", "how should the agent route between these skills" or "revise my agent's orchestration text". Do not use to review a single SKILL.md, use skill-file-reviewer instead. Drafts for human review; never approves, authorises or signs off.
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Read and modify MIKE+ model parameters via the mike-plus MCP server — inspect or change values in any model table (pipe Diameter/Manning in msm_Link, node levels in msm_Node, catchment properties in msm_Catchment, etc.). Use when an agent must identify a current parameter and change it before re-running. Requires MIKE+ + license; mutating tools must run on a copy.
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When the user wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.
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Reviews Laravel migrations for destructive operations, change() dropping modifiers, locking index creation on large tables (PostgreSQL), and asymmetric down().
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Generate PHP/Laravel (PHPUnit) test skeletons from specifications.
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Detects N+1 query patterns (relation access inside loops without eager loading), full-table get()/all() loads, and unsafe chunk()/cursor() usage in Laravel Eloquent code.
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Detects mass assignment via create/update($request->all()), unguarded models, and missing authorization on mutating controller actions in Laravel.
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6つの専門レビュアーロールを並列実行し、consensusLevel（複数ロールの合意度）と Tech Lead レポート（top3指摘・blindSpots・consensusSummary）で結果を統合する マルチエージェントレビュー entry skill。 Parallel multi-role review with consensus scoring (consensusLevel) and Tech Lead report. Use when a major release needs exhaustive multi-angle review, or when a single-perspective review is not enough and you want confidence that no reviewer angle was missed（重要リリース前の網羅レビュー・多視点の確証が 欲しいとき）。
Route OpenClaw design work to canonical brand, Carapace product-interface, marketing-page, or design-audit guidance. Use when a task touches OpenClaw visual identity, shared CSS tokens, product UI, public web pages, or Carapace compliance.
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Implements type-safe Flutter routing using AutoRoute v11 with code generation and @RoutePage annotations. Activates when setting up AutoRoute navigation, configuring AutoRouterConfig or RootStackRouter, generating route files with build_runner, using @RoutePage, @PathParam, or @QueryParam annotations, implementing AutoTabsRouter for bottom navigation tabs, adding navigation guards with AutoRouteGuard, setting up nested or child routes, handling type-safe route parameters, or migrating from string-based Navigator routing to compile-time-verified routes. Ideal for large apps requiring refactoring confidence and zero string-based routing errors.
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