223 个 · 按相关度排序
Use when working with Aws Dynamodb Deep — aWS DynamoDB deep-dive management covering table configurations, capacity analysis, GSI/LSI health, auto-scaling policies, streams, backups, contributor insights, and TTL settings. Use when performing deep analysis of DynamoDB tables, optimizing capacity and throughput, debugging throttling, or auditing table configurations beyond basic inventory.
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Use when working with Aws Kinesis — aWS Kinesis stream management, shard analysis, consumer lag monitoring, enhanced fan-out configuration, and throughput analysis.
中文简介正在准备中。当前显示原文说明。
Use when working with Azure Eventhubs — azure Event Hubs partition analysis, checkpoint management, capture status, consumer group monitoring, and throughput analysis.
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Use when working with Azure Cosmos Db — azure Cosmos DB throughput analysis, partition key distribution, consistency levels, metrics monitoring, and database management via Azure CLI.
中文简介正在准备中。当前显示原文说明。
Generates as many digits of pi as possible and writes them into files inside a tokenMaxxing/ folder. Use when the user asks to token-maxx, fill a file with pi digits, test agent output throughput, or generate N pi files sequentially or in parallel.
中文简介正在准备中。当前显示原文说明。
Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.
中文简介正在准备中。当前显示原文说明。
Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.
中文简介正在准备中。当前显示原文说明。
High-throughput event streaming and real-time data ingestion.
中文简介正在准备中。当前显示原文说明。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
中文简介正在准备中。当前显示原文说明。
Focused Signals scout for PostHog projects running Replay Vision scanners — the standing LLM probes that watch session recordings and write `$recording_observed` events. Watches two promises: that enabled scanners are actually observing (throughput / success-rate cliffs, exhausted quota — a silent watch gap), and that what the scanners see in aggregate gets surfaced (a monitor's `yes`-rate or a scorer's score stepping away from its own baseline, a classifier tag or a recurring summarizer theme concentrating across many sessions). It is the agentic pull complement to the per-session push path: scanners with `emits_signals` already emit one signal per session into this same inbox, so this scout never repeats them — it adds the cross-session shape the per-session probe can't see. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.
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Diagnoses CI and pull-request pipeline health for a GitHub repo using the engineering analytics MCP tools — pull-requests (PR list with CI status), workflow-health (per-workflow CI trends), and pr-lifecycle (a single PR's timeline). Use when asked whether CI is getting faster or slower, which GitHub Actions workflow is the slow or flaky long-pole, how long PRs take from open to merge, how an author's merge time compares to the cohort, which open PRs have failing or pending CI, or where a specific pull request is stuck. Triggers on "engineering analytics", "is CI getting slower", "slow workflow", "flaky CI", "time to merge", "cycle time", "PR throughput", "failing checks", "where is PR <n> stuck", "CI long pole", "what's holding up this PR".
中文简介正在准备中。当前显示原文说明。
Optimize AI system latency and throughput. TRIGGERS - Use when user needs help with ai-latency-optimizer related tasks.
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Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.
中文简介正在准备中。当前显示原文说明。
High-throughput event streaming and real-time data ingestion.
中文简介正在准备中。当前显示原文说明。
Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.
中文简介正在准备中。当前显示原文说明。
Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.
中文简介正在准备中。当前显示原文说明。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
中文简介正在准备中。当前显示原文说明。
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
中文简介正在准备中。当前显示原文说明。
High-throughput event streaming and real-time data ingestion.
中文简介正在准备中。当前显示原文说明。
Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.
中文简介正在准备中。当前显示原文说明。
Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.
中文简介正在准备中。当前显示原文说明。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
中文简介正在准备中。当前显示原文说明。
Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.
中文简介正在准备中。当前显示原文说明。
Coordinate parallel autonomous operations. Use when running parallel features, managing concurrent work, coordinating multiple agents, or optimizing throughput.
中文简介正在准备中。当前显示原文说明。