“bottleneck”的搜索结果

Diagnose MSBuild build performance bottlenecks using binary log analysis. Only activate in MSBuild/.NET build context. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, errors, search, items, properties); falls back to dotnet msbuild binlog replay + grep/cat when the MCP is unavailable.

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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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Measure, profile, and fix performance bottlenecks — Core Web Vitals, backend latency, bundle size, and database queries. Load when performance requirements exist, users report slowness, profiling reveals bottlenecks, or the user asks to optimize load time, LCP, INP, CLS, or response time. Not for premature optimization without evidence. Pairs with browser-testing-with-devtools and ci-cd-and-automation for regression guards.

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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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Codified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines.

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Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".

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Identifies performance bottlenecks and provides ordered scaling strategies with triggers, phased plans, and cost implications. Use for "scalability planning", "performance bottlenecks", "capacity planning", or "growth strategy".

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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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从服务运维中的实际问题出发选择指标，为Grafana或SigNoz创建监控仪表板，按用途组织面板，帮助判断服务健康状况、延迟、吞吐量和资源瓶颈。

• 创建Kafka监控仪表板
• 监控Elasticsearch状态和性能
• 监控API gateway延迟和错误

指导使用Axolotl对大语言模型进行微调，涵盖YAML配置、数据格式和训练方法，也可用于检查多GPU设置、排查训练故障及运行缓慢的问题。

• 编写微调用的YAML配置
• 了解LoRA和QLoRA训练方法
• 查阅数据格式和API用法

通过测量定位页面显示、交互、API和数据库的性能瓶颈，对比修改前后的数据验证效果。撤销没有明确收益的修改，并添加性能检查与监控，防止再次变慢。

• 调查页面加载和交互迟缓的原因
• 减少API延迟与重复查询
• 检查修改后的性能下降

Analyze application and system performance to find bottlenecks, the points that slow processing. Use resource usage and performance data to plan improvements to resource allocation, load balancing, caching, and algorithms, then compare results after changes.

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• Investigating slow application processing
• Reviewing CPU and memory allocation
• Improving workload distribution across nodes

Improve existing code through repeated cycles of testing, implementation, and refactoring. The skill covers performance bottlenecks, error recovery, code coverage, and documentation, helping developers refine behavior and structure while keeping tests passing.

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• Refactoring existing code while keeping tests passing
• Adding tests before implementing expected behavior
• Investigating slow database access and cache usage

Monitor agent and system performance through live metrics, resource tracking, bottleneck analysis, and anomaly detection. Track response times and availability against service targets, and prepare dashboard data and performance reports.

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• Investigate slow agent coordination
• Track CPU and memory usage
• Monitor response times against service targets

Coordinate tasks across multiple AI agents by tracking workloads and capacity. Redistribute queued work to available agents, adjust priorities and deadlines, and monitor resource use to identify uneven loads and scheduling bottlenecks.

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• Redistributing tasks from busy agents to available agents
• Scheduling queued tasks by priority and deadline
• Investigating queue delays and uneven workloads

Analyze networks of people, web pages, or systems using PageRank, a method that scores importance from connections. Identify influential nodes and communities, generate personalized recommendations, and examine communication bottlenecks to guide network improvements.

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• Finding influential users and communities on social networks
• Analyzing links and page importance on a website
• Generating recommendations based on user preferences

Analyze slow development workflows and agent coordination using execution times, resource use, dependencies, and communication patterns. Identify bottlenecks, prioritize causes, and build an improvement plan covering parallel work, caching, or resource allocation.

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• Investigate slow test execution
• Find unnecessary waits in task sequences
• Reduce communication delays between agents

Inspect how a task ran across agents by collecting recorded execution spans, or timed processing steps. Build a trace tree showing agent activity, duration, errors, and bottlenecks, with a summary of the execution flow.

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• Investigating a slow task
• Reviewing coordination between agents
• Locating errors in recorded execution

Analyze performance in Claude Flow swarms, groups of agents working together. Identify delays in communication, processing, memory, and networking, then generate reports and prioritized recommendations. Compare previous runs or apply available automatic optimizations.

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• Investigating slow tasks across multiple agents
• Comparing performance before and after changes
• Creating weekly performance reports

Measure page loading speed and identify what prevents a page from loading in under three seconds. Use performance measurements to guide improvements such as lazy loading, caching, CDN delivery, and reducing resource costs.

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• Investigating slow page loads
• Finding heavy resources and costly rendering steps
• Checking performance on a throttled 3G connection

Audit page loading and choose resource hints, browser instructions for fetching files or preparing connections early. Use measurements to identify delays, then suggest preload, preconnect, prefetch, or dns-prefetch for critical assets, external services, and likely next-page resources.

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• Reviewing early loading of key images and fonts
• Preparing connections to external services
• Prefetching resources for likely next pages

Measure the total size of a web page and its resources, identify what slows loading, and reduce unnecessary weight. Use a 1500KB limit to guide image optimization, code compression, and removal of unused resources.

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• Investigating slow page loads on mobile networks
• Checking total page size against a size budget
• Reducing heavy images and JavaScript files

Review early connections to third-party services such as fonts and APIs. Use loading measurements to identify essential domains, recommend preconnect hints that prepare connections in advance, and remove unnecessary hints that add overhead.

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• Investigating connection delays for external fonts and APIs
• Reviewing preconnect hints in the page head
• Removing unnecessary early connections

Inspect compressed JavaScript bundles, the files that package application code, to find excessive size and loading costs. Recommend splitting code, removing unused portions, and reviewing dependencies based on measured effects on page responsiveness.

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• Finding JavaScript files over 200KB after compression
• Investigating delays before a page becomes interactive
• Reducing bundle size through code splitting