「voc」の検索結果

Biblioteca do Facebook para busca eficiente de similaridade e clustering de vetores densos. Suporta bilhões de vetores, aceleração GPU e vários tipos de índice (Flat, IVF, HNSW). Use para busca k-NN rápida, recuperação de vetores em larga escala ou quando você precisa de busca pura de similaridade sem metadados. Melhor para aplicações de alto desempenho.

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Otimiza inferência de LLM com NVIDIA TensorRT para máxima vazão e latência mínima. Use para implantação em produção em GPUs NVIDIA (A100/H100), quando você precisa de inferência 10-100x mais rápida que PyTorch, ou para servir modelos com quantização (FP8/INT4), batching em voo e escalabilidade multi-GPU.

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Framework de pré-treinamento visão-linguagem que conecta codificadores de imagem congelados e LLMs. Use quando você precisar de legendagem de imagens, resposta a perguntas visuais, recuperação imagem-texto ou chat multimodal com desempenho zero-shot de última geração.

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Quantização pós-treinamento em 4-bits para LLMs com perda mínima de precisão. Use para implantar modelos grandes (70B, 405B) em GPUs de consumo, quando você precisa de redução de memória 4× com <2% de degradação de perplexidade, ou para inferência mais rápida (aceleração de 3-4×) vs FP16. Integra com transformers e PEFT para fine-tuning QLoRA.

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Instâncias GPU em nuvem reservadas e sob demanda para treinamento e inferência de ML. Use quando você precisar de instâncias GPU dedicadas com acesso SSH simples, sistemas de arquivos persistentes ou clusters multi-node de alto desempenho para treinamento em larga escala.

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Plataforma GPU serverless em nuvem para executar workloads de ML. Use quando você precisar de acesso GPU sob demanda sem gerenciamento de infraestrutura, fazendo deploy de modelos de ML como APIs, ou executando jobs em batch com auto-scaling.

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Geração estruturada rápida e serving para LLMs com RadixAttention para cache de prefixos. Use para saídas JSON/regex, decodificação restrita, workflows com agentes que fazem chamadas de ferramentas, ou quando você precisa de 5× mais rápido que vLLM com compartilhamento de prefixos. Alimenta 300.000+ GPUs na xAI, AMD, NVIDIA e LinkedIn.

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Memória é a base de agentes inteligentes. Sem ela, cada interação começa do zero. Esta skill cobre a arquitetura de memória de agentes: curto prazo (janela de contexto), longo prazo (vector stores), e as arquiteturas cognitivas que as organizam. Insight-chave: Memória não é apenas armazenamento - é recuperação. Um milhão de fatos armazenados não significam nada se você não conseguir encontrar o certo. Estratégias de chunking, embedding e recuperação determinam se seu agente se lembra ou esquece. O campo é fragm

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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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[User-Invoked] Use ONLY when the user explicitly types /dual-ai or /dual-ai <workflow-id> — fans out one prompt, or one workflow invocation per tool, to two fresh parallel AI sessions (Claude Code + Codex CLI), both pre-set to xhigh reasoning effort and full-permission mode before the prompt executes. NEVER auto-activate.

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Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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Build, audit, and modify EMULATORS runtime `.fmt` formatter files and keep them synchronized with C++ `getInfoStructure()` producers and their `.hpp` field documentation. Use when adding, renaming, or removing `InfoStructure` attributes or nested structures; diagnosing missing console fields; reviewing formatter includes, formatter names, placeholders, arrays, nested invocations, or table parameters; or changing namespace-specific `.fmt` files under `projects`.

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Scrub English drafts for AI tells before publishing. Use for any English LinkedIn post or newsletter draft right after composition and BEFORE export. Removes em-dash-as-pause, weak hedges, rule-of-three patterns, AI vocabulary, negative parallelisms, and inflated symbolism. Triggers on "polish", "humanize", "make this sound human", "/polisher", or after any English composer output. Leaves Arabic and Hebrew drafts untouched — humanizer-* skills handle those.

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Generate AI music and songs with Diffrythm, Tencent Song Generation via inference.sh CLI. Models: Diffrythm (fast song generation), Tencent Song Generation (full songs with vocals). Capabilities: text-to-music, song generation, instrumental, lyrics to song, soundtrack creation. Use for: ba...

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Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.

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Detect bootkits such as BlackLotus and Bootkitty and Secure Boot bypass via DBX and binary checks.

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Shared vocabulary for designing deep modules, interfaces, seams, adapters, leverage, and locality. Use when designing or reshaping module boundaries, making code more testable, or evaluating interface depth.

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Use when the user explicitly invokes the `if` skill or asks for a concise, laconic, no-fluff, or to-the-point answer. Do not trigger on the ordinary word or programming keyword `if`. Detailed, step-by-step, exhaustive, educational, or fixed-length requests activate only when they also include an explicit invocation or brevity request.

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Work the review of a research run with rs_review — read the manuscript and record the panel's concerns with the evidence that grounds each one, keep the author's reply and the re-review's finding in their separate columns, run the devil's advocate with the concession threshold the tool enforces, and write the panel report and traceability matrix a response-to-reviewers letter is built from. Use when a document is ready for review, when a concern has to be answered or re-reviewed, or when an attack or a concession is being recorded.

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Mine Claude Code session data for analytics — token usage, tool rankings, skill invocations, session search, and prompt history. Use when asked about session stats, token costs, tool frequency, skill usage patterns, or finding past sessions by prompt text.

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Gauntlet for absolute claims (always/never/guaranteed/optimal); pressure-test, then refine with explicit boundaries. Use when users ask to prove or disprove strong certainty claims, request devil's-advocate challenge rounds, or want the /tw-prove-it gauntlet to run all 10 rounds continuously by default.

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Knowledge base synthesized from the Guide to the Systems Engineering Body of Knowledge (SEBoK) v2.14 by the BKCASE Project. Use for the established systems-engineering canon: SE fundamentals and foundations (systems science, systems thinking, the systems approach, modelling/MBSE), the life cycle (ISO/IEC/IEEE 15288/24748/24774 process, development & agile approaches, model selection/tailoring), technical management, concept definition, architecture/realization (the Vee), V&V, maintenance/sustainment, SE standards, and the application contexts (product, service, enterprise, systems-of-systems, healthcare), plus enabling SE (business/team/individual), related disciplines, quality attributes/specialty engineering, implementation examples, and emerging knowledge. Good for grounding background, vocabulary, and prior-art positions across all eight SEBoK Parts. SCOPE LIMITS: this pack is SEBoK v2.14 only (released 2026-05-18); it synthesizes and signposts rather than reproducing the source — it does not reproduce ISO/IEC/IEEE standard text, is a curated guide (not a complete textbook), and is thin on AI-integration depth (Part 8 is a staging area, not settled practice). LICENCE: content is CC BY-NC-SA 3.0 — non-commercial use only, attribution to the BKCASE Project required, share-alike obligations carry forward.

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Knowledge base from the NASA Fault Management Handbook (NASA-HDBK-1002, Draft 2, April 2012). Use for the systems-engineering discipline of designing what a system does when it fails — the fault/failure/anomaly vocabulary, the five FM strategies (two prevention strategies — design-time fault avoidance and operational failure avoidance — plus three tolerance strategies — failure masking, failure recovery, and goal change), the six FM process activities across NASA mission phases Pre-A to E, response-latency-versus-time-to-criticality (TTC) design trades, fault/failure containment regions and FEPPs, the four redundancy approaches, top-down FM requirements development, FM verification vs. validation, the seven dedicated FM milestone reviews (FMCR/FMARR/FMPDR/FMCDR/FMTRR/FMLRR/FMCERR), FM organizational structure, and mined NASA Lessons Learned. Space-mission oriented (flight/ground/operations); spacecraft examples throughout. IMPORTANT: built from an UNAPPROVED DRAFT — several sections (detailed architecture building blocks, the Assessment & Analysis guidance, and Operations & Maintenance) are placeholders in the source and are thin here. Thin on detailed FDIR algorithms, terrestrial/IT fault management, and any approved-standard terminology that may have changed since Draft 2.

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Knowledge base from NASA-STD-8729.1A — NASA's R&M (Reliability and Maintainability) standard covering spaceflight and support systems (Revision A, 2017-06-13). Use for NASA's objectives-driven R&M framework — the R&M objectives hierarchy (one Top Objective decomposed into four sub-objectives, paired with tailorable strategies), how R&M requirements are established inside the SMA Plan required by NPR 7120.5, the SMA Technical Authority concurrence/independent-evaluation governance, milestone vs. readiness review gates, the reliability/maintainability/availability vocabulary (Ai vs. Ao, the failure causal chain, risk as a triplet), and the R&M Evidentiary Methods catalogue (FMEA/FMECA, FTA, RBDA, RCM, LORA, plus the space-environment and parts-pedigree analyses). Scoped to NASA spaceflight and support systems and to assurance planning: it is an objectives-and-strategies standard, so it deliberately does NOT prescribe step-by-step analysis procedures (those live in the referenced NASA Preferred Reliability Practices) and is thin on facility R&M, detailed math/derivations, and non-NASA or commercial life cycles.

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