「latency」の検索結果

Query the witness-anchored ruLake cache — search, verify, explain, or refresh. Returns ranked results plus a decision_trace block with cost, latency, witness match, and substrates used.

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。

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

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。

Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).

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。

Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.

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。

Banco de dados vetorial gerenciado para aplicações de IA em produção. Totalmente gerenciado, com auto-scaling, busca híbrida (vetores densos + esparsos), filtragem de metadados e namespaces. Latência baixa (<100ms p95). Use para RAG em produção, sistemas de recomendação ou busca semântica em escala. Ideal para infraestrutura serverless e gerenciada.

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。

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.

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。

Acelere a inferência de LLMs usando especulative decoding, múltiplas cabeças Medusa e técnicas de lookahead decoding. Use ao otimizar velocidade de inferência (aceleração de 1,5-3,6×), reduzir latência em aplicações em tempo real ou fazer deploy de modelos com recursos computacionais limitados. Cobre modelos draft, atenção em árvore, iteração de Jacobi, geração paralela de tokens e estratégias de deploy em produção.

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。

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

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。

TLS 1.3 (RFC 8446) is the latest version of the Transport Layer Security protocol, providing significant improvements over TLS 1.2 in both security and performance. It reduces handshake latency to 1-R

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。

Analyze network device logs to identify connectivity issues, latency problems, and outages. Correlate timestamps across timezones, detect anomaly patterns, and generate incident reports with root cause hypotheses. Use when troubleshooting network incidents from log files.

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。

Judge performance or resource claims on real hardware: benchmark design or review, latency, throughput, memory or headroom numbers, and accept-or-reject decisions on optimizations. Not for refactors with no resource claim.

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。

Audit over-engineered codebases by factoring layers into live obligations, quotienting redundant distinctions, ablating unearned surface, and normalizing survivors while preserving required behavior. Use when change latency or agent difficulty comes from frameworks, plugins, DI, codegen, task runners, config indirection, ORMs, GraphQL, monorepo/infra tooling, or web stacks, or when asked to remove layers. Produces an evidence-backed Reduction Certificate, cuts, migration phases, proof signals, and rollback. Not for one local readability cleanup (use tw-complexity-mitigator).

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。

Capture and compare Clawix UI performance budgets for critical flows before optimizing visible or runtime UI behavior.

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。

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.

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。

Knowledge base from the FAA Requirements Engineering Management Handbook (DOT/FAA/AR-08/32). Use for writing requirements for real-time, embedded control systems — the avionics/medical slant — via the Handbook's eleven recommended practices: system overview and boundary (monitored/controlled variables), operational concepts (use cases), environmental assumptions, change-tolerant functional architecture (dependency diagrams), safety-driven architecture revision (FHA/PSSA/fault tree), system modes, detailed behavior and performance requirements (ideal value + tolerance + latency), the four-variable model bridge to software requirements (MON/CON/NAT/REQ, IN'/REQ'/OUT', DO-178B mapping), subsystem allocation, and rationale. Carries the Isolette Thermostat and Flight Control System worked examples. Slanted toward control systems with sensors and actuators; thin on information-system / enterprise / agile requirements, on requirements-management tooling, and on the formal-methods internals of SCR/RSML/SpecTRM (it names them, does not re-teach them). Does not reproduce the IEEE/DO-178B/ARP standards it cites.

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。

Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.

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。

Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

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。

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

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。

Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.

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。

Use when working with Banana Dev — banana.dev ML inference platform management covering model inventory, deployment status, API call history, GPU allocation, scaling configuration, build logs, and latency metrics. Use for comprehensive Banana.dev model deployment assessment and inference performance analysis.

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。

Use when working with Apache Pinot — apache Pinot management — monitor cluster health, tables, segments, ingestion jobs, query performance, and controller/broker/server status. Use when debugging ingestion issues, inspecting segment distribution, optimizing query latency, or auditing cluster capacity.

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。

Use when working with Apache Druid Deep — apache Druid deep management — monitor cluster health, datasources, ingestion tasks, segments, query performance, and coordinator/overlord status. Use when debugging ingestion failures, inspecting segment distribution, optimizing query latency, or auditing cluster capacity.

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。

Use when working with Aws Xray — aWS X-Ray trace analysis, service map generation, fault and error analysis, sampling rule management, and latency investigation. Covers trace summaries, segment analysis, annotation-based filtering, and group configuration.

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。

Use when working with Gcp Cloud Trace — google Cloud Trace latency analysis, trace exploration, sampling configuration, and span diagnostics via gcloud CLI.

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。