279 件 ・ 関連度順
概要と使いどころ
Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).
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。
Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.
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。
Guides the 7-step MATLAB memory optimization workflow: baseline, profile, identify, optimize, measure, verify, report. Use when asked to reduce MATLAB memory usage, find memory bottlenecks, fix out-of-memory errors, or optimize memory-intensive code.
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。
Supply Chain Bottleneck Analyzer for Shopify/DTC stores. Diagnose cash flow, inventory, shipping costs, and customer acquisition efficiency. Includes CAC/LTV analysis, 3PL cost optimization, and ad spend benchmarks. No API key required for basic 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。
Supply Chain Optimization (Lite) - Diagnose bottlenecks and provide cost reduction strategies through conversation
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。
Supply Chain Bottleneck Analyzer for Walmart Marketplace sellers. Diagnose cash flow, inventory, WFS costs, and referral fees. Includes comparison with Amazon FBA, lower storage fee optimization, and Walmart Connect ad spend analysis. No API key required for basic 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。
Supply Chain Bottleneck Analyzer for TikTok Shop sellers. Diagnose cash flow, inventory turnover, affiliate commissions, and return rates. Includes FBT cost analysis, influencer payout optimization, and viral product lifecycle management. No API key required for basic 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。
Report concurrency and parallelism for a session — how many agents ran in parallel, concurrency-lane utilization, peak parallel width, and serialization bottlenecks (sequential chains that could have run as parallel lanes) — using the Agent Monitor workflow intelligence API. Use when checking whether a multi-agent session used parallelism efficiently.
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。
Optimize LuisaCompute DSL kernels using warp/wave primitives, shared-memory aggregation, and block-level collectives. Use when kernels bottleneck on atomics, reductions, or inter-thread communication.
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。
When the user wants to plan growth using the AARRR framework, diagnose growth bottlenecks, or map actions across the customer lifecycle. Also use when the user mentions "growth funnel," "AARRR," "pirate metrics," "acquisition activation retention," "customer lifecycle metrics," or "growth framework." For retention tactics, use retention-strategy.
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 investigating slow activities, waiting, handoff duration, bottlenecks or cumulative activity workload.
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。
Debug and optimize Android/Gradle build performance. Use when builds are slow, investigating CI/CD performance, analyzing build scans, or identifying compilation bottlenecks.
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。
Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Read-only. Surfaces host-side signals (Xid, ECC, NVLink, EFA reachability, FSx saturation) and routes to the appropriate sibling skill (hyperpod-node-debugger, hyperpod-nccl, hyperpod-version-checker, hyperpod-issue-report) for any remediation. Triggers on uneven NCCL across nodes, straggler node, FSx slow, checkpoint slow, dataloader slow, filesystem bottleneck, FSx throughput, cross-AZ latency, topology mismatch.
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。
Personal leadership development for founders and first-time CEOs. Use when a founder feels like the bottleneck, struggles to delegate, is burning out, or transitioning from IC to executive.
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 optimizing Godot games — profiler, draw calls, physics tuning, memory management, and common bottlenecks
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。
Guide performance profiling with Instruments, diagnose hangs, memory issues, slow launches, and energy drain. Use when reviewing app performance or investigating specific bottlenecks.
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。
Profiles and improves a measured performance bottleneck; retains only verified improvements.
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。
Optimize NestJS throughput with Fastify adapter, singleton scope enforcement, compression, and query projections. Use when switching to Fastify, diagnosing request-scoped bottlenecks, or profiling API overhead.
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。
Enforce universal standards for high-performance development. Use when profiling bottlenecks, reducing latency, fixing memory leaks, improving throughput, or optimizing algorithm complexity in any language.
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。
When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.
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 dotnet-diagnostics-mcp for MCP-driven .NET CPU, allocation, GC, and test-host profiling. USE FOR: profiling unit tests or investigating runtime bottlenecks. DO NOT USE FOR: writing test assertions, coverage collection, or precise microbenchmark timing. INVOKES: connect the MCP server, select target processes, collect bounded profiles, and attribute hotspots to source.
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。
Establish MSBuild/.NET build performance baselines before optimizing. USE FOR: a .NET build or solution that is slow or has build-performance concerns; cold, warm, incremental, or no-op measurements; before/after comparisons; CI build output caching; static graph build decisions; artifacts output; and dependency graph trimming. Start here before build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE for webpack, npm, JavaScript bundlers, or other non-MSBuild build systems. For detailed target/task/analyzer bottleneck analysis after baselining, use build-perf-diagnostics.
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。
Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, comparing segments through a funnel, or optimizing user journeys.
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 you need to analyze Java profiling data collected during the detection phase — including interpreting flamegraphs, memory allocation patterns, CPU hotspots, threading issues, systematic problem categorization, evidence documentation with profiling-problem-analysis and profiling-solutions markdown files, or prioritizing fixes using Impact/Effort scoring. This should trigger for requests such as Analyze JFR profile; Analyze the profile; Analyze the performance; Analyze the memory; Analyze the threading; Analyze GC logs from profiling; Prioritize Java profiling bottlenecks by impact. 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。