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Analyze live or captured DOCA Flow pipelines to guide changes to rule placement, table sizing, and hardware offloading. The skill helps interpret snapshots, compare measurements before and after changes, and investigate missing or inconsistent tuning results.
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Measure how quickly a host or DPU CPU installs and deletes DOCA Flow rules. Choose a JSON workload and backend, run repeated benchmarks, and record hardware, software, and execution settings so results can be assessed.
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Investigate Google Cloud network problems using traffic logs, performance metrics, and connection path tests. Identify blocked traffic, packet loss, and latency, summarize traffic volumes, or estimate VPC Flow Logs costs.
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分析claude-flow v3的MCP服务器延迟与负载，协助实现连接复用、工具检索、负载均衡和通信改进，并监控响应时间、错误率、内存及缓存表现。
分析claude-flow v3的MCP服务器延迟与负载，协助实现连接复用、工具检索、负载均衡和通信改进，并监控响应时间、错误率、内存及缓存表现。
Improve the MCP server that connects claude-flow v3 to tools. It guides connection reuse, faster tool lookup, request distribution, and communication changes, with monitoring to evaluate response times, errors, memory use, and cache performance.
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通过操作次数与实测数据排查OpenClaw启动、聊天和界面显示缓慢的原因，优先减少不必要的处理，并支持性能改进PR的编写与审查。
Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.
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Design systems where multiple AI agents share work, generate tool schemas that define tool inputs for Anthropic and OpenAI, and analyze execution logs. Outputs include role assignments, communication diagrams, and recommendations for addressing cost, latency, and failures.
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Investigate slow or failing requests in Google Cloud using Cloud Trace. Filter traces by duration or error status, inspect spans representing individual operations, and retrieve related Cloud Logging entries to investigate bottlenecks and exceptions.
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Test how an app handles slow or failing dependencies by adding delays and connection errors to outgoing TCP connections in a mirrord session. Create reusable rules, manage them during development, and include them in CI tests.
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Add OpenTelemetry tracing to frontend and backend flows. Connect execution records across services and background jobs, record phase durations and entity IDs, and mark failures so developers can investigate slow or broken operations.
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ネットワーク品質を総合的に診断し、ボトルネックの特定と根本原因の深堀りまで行うスキル。 接続情報、レイテンシ、ダウンロード速度、HTTP接続タイミング、経路解析を自動実行し、 品質閾値に基づく総合評価を日本語レポートとして出力する。 外部依存なし（OS標準ツールのみ使用）、macOSおよびLinux対応。 Use when diagnosing network quality, troubleshooting slow connections, identifying network bottlenecks, measuring latency/bandwidth/jitter, or generating network health reports. 「ネットワーク診断」「network diagnostics」「ネットワークが遅い」 「latency check」「bandwidth test」「traceroute analysis」
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审查REA的分析流程与缓存设计，在保持结果正确性和依据可访问性的同时，改善耗时、CPU与内存使用，以及智能体需要处理的信息量。
以只读方式检查Caveman Cloud的费用、响应时间和错误，对照汇总数据与单次执行记录，依据证据整理支出构成及可改进之处。
Plan voice AI systems by comparing direct speech processing with separate transcription, language generation, and speech synthesis stages. Covers response delays, detecting when people speak, interruptions, and noise handling to guide conversational interface design.
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Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu
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Identifies API latency hotspots and bottlenecks with profiling tools, slow endpoint detection, suspected causes, and fix roadmap. Use for "latency profiling", "performance bottlenecks", "slow APIs", or "backend performance".
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根据任务类型将后台工作分配给专业智能体，利用执行历史中的质量评分、成功率、平均响应时间和执行次数辅助选择负责人，并查看性能及协作统计。
Build cryptocurrency exchange integrations in Rust with CCXT. Follow examples for fetching prices and order books, placing orders, streaming live updates through WebSocket connections, and handling authentication and errors with typed results.
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Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.
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Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.
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Investigate cmux interface and runtime problems using debug logs, profiling, and the Debug menu. It guides changes to terminal rendering and tab lists, with checks for typing latency, SwiftUI state handling, and differences between macOS versions.
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Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.
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