「bottleneck」の検索結果

Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.

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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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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).

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Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean / Six Sigma / Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.

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Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-observability).

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AI Berkshire skill: 供应链瓶颈猎手：AI驱动的全球产业链瓶颈套利. Source: skills/bottleneck-hunter.md.

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Diagnose MSBuild build performance bottlenecks using binary log analysis. 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.

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Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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[omh] Software slowness, memory leaks, or cost spikes: find where a system is actually slow, leaking, or expensive across runtime, memory, token cost, storage, rendering, inference, CI, and query domains, then fix one measured hot path at a time behind a regression budget. Use when the user says: ultraperf, ulw-perf, performance audit, performance bottleneck, find the bottleneck, profile the hot path, memory leak investigation, token cost hotspot.

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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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Map, measure and improve business processes — SIPOC and swimlane capture, cycle-time and bottleneck analysis, handoff diagnosis, and a payback-ranked improvement backlog. Use when a process is slow, error-prone, or crosses too many teams.

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Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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A股/港股/美股通用"卡脖子"选股技能。应用 Serenity（@aleabitoreddit）的供应链瓶颈理论， 对任意板块/产业链进行结构化分析，寻找"一旦断货整个产业就停工"的瓶颈环节， 并筛选该环节中具备技术壁垒和资本信号的小盘股。 触发词：分析XX板块、找XX卡脖子、serenity分析、A股/美股/港股瓶颈产业链 EN triggers: analyze [sector] choke points / find bottlenecks in [industry] JP triggers: [セクター] のボトルネックを分析 KR triggers: [산업] 의 병목 지점 분석 适用板块示例：电力、光模块、医疗器械、半导体设备、军工、新能源车等任意板块； 港美股示例：InP衬底（AXTI）、CPO激光器（AAOI）、AI算力链（NVDA/TSM） 数据源：三级数据获取策略（智能体自带财经工具 → 本技能内置脚本 → 网络检索）， 不依赖任何特定平台，Claude Code / Codex / 通用智能体均可直接使用。 行情/估值支持 A股+港股+美股；研报/两融/筹码等深度数据仅A股。 ⚠️ 注意：本技能不预测大盘走势，不适用于纯题材炒作无实质产业逻辑的小票。

Diagnose MSBuild build performance bottlenecks using binary log analysis. 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.

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Use when you need to refactor Java code based on trusted profiling analysis findings — including reviewing repository-owned or maintainer-sanitized docs/profiling-problem-analysis and docs/profiling-solutions files, identifying specific performance bottlenecks, and implementing targeted code changes to address CPU, memory, or threading issues. This should trigger for requests such as Refactor the code with profiling; Apply profiling; Optimize hot path; Reduce allocations found in profiling; Fix CPU bottlenecks from profiling analysis. Part of Plinth Toolkit

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。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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 C++ build performance using vcperf.exe and ETL traces. Use this skill when the user wants to profile a build, understand compile bottlenecks, identify expensive headers or templates, generate JSON analysis reports, or interpret vcperf output. Covers tracing workflows, the /jsonAnalysis flag, reading the resulting JSON schema, and actionable remediation strategies for each bottleneck category.

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Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance reports with flame graphs and recommendations.

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Performance optimization mindset - knowing when to optimize, how to measure, where bottlenecks hide, and when "fast enough" is the right answerUse when "slow, performance, optimize, profiling, benchmark, latency, throughput, cache, n+1, bottleneck, memory leak, too slow, speed up, response time, performance, optimization, profiling, caching, latency, throughput, big-o, benchmarking" mentioned.

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。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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。

Deeply analyzes application architecture and structure to perform audit, bottleneck detection, and code/performance optimization / Mempelajari arsitektur dan struktur aplikasi secara mendalam untuk melakukan audit, deteksi bottleneck, serta optimasi performa dan kode.

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。

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.

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。