569 件 ・ 関連度順
概要と使いどころ
Background legal and regulatory context for the shinkoku tax filing plugin. Contains the standard disclaimer about the scope of tax information provided, the relationship to the Tax Accountant Act (税理士法), and tool limitations. This skill is not user-invocable — Claude loads it automatically when generating tax-related responses that require a disclaimer.
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How to find and fix performance problems in Shift's desktop app. Use when something is slow, choppy, janky, or laggy (scrubbing, dragging, editing, undo, opening fonts), when profiling or measuring, when adding or reviewing perf tests, and before claiming a change made something faster.
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。
You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched
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。
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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 a clinical CSV/Excel dataset needs profiling and cleaning before analysis (missing values, outliers, duplicates, type mismatches). Profiles, flags and generates cleaning code in three stages, each gated on the researcher's approval. Never auto-cleans.
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。
Native macOS/iOS app performance profiling via xctrace/Time Profiler and CLI-only analysis of Instruments traces. Use when asked to profile, attach, record, or analyze Instruments .trace files, find hotspots, or optimize native app performance without opening Instruments UI.
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。
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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 DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational 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。
Use MosaicML Composer for PyTorch training loops, speedup methods, loggers, checkpoints, distributed launch, profiling, and model export.
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。
Financial trading CLIs — spot and perp trading on Hyperliquid, Binance, Coinbase, OKX. Prediction markets on Polymarket. Deposit and withdraw across chains. LLM-enriched price quotes with trend analysis. News and SEC filings. Historical backtesting with forward PnL analysis. Use when: user asks about stock/crypto prices, wants to trade, deposit, withdraw, check portfolio, or backtest a 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。
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
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 multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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。
RBG controller stress test - deploy kwok in existing cluster, run create/update/delete load tests with real controller pod, collect pprof profiling and logs, generate analysis report with recommendations
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。
Implements the UK Age Appropriate Design Code (Children's Code) 15 standards under the Data Protection Act 2018 Section 123. Covers best interests assessment, age-appropriate application, transparency, data minimization, geolocation restrictions, and profiling defaults. Keywords: AADC, Children's Code, ICO, age appropriate design, UK.
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。
Manages the absolute right to object to direct marketing under GDPR Article 21(2)-(3), covering immediate cessation of all direct marketing processing, suppression list management, cross-channel enforcement, and profiling for marketing purposes. Activate for marketing opt-out, unsubscribe, Art. 21(2), direct marketing objection queries.
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。
Nigeria Data Protection Regulation (NDPR) and Nigeria Data Protection Act (NDPA) 2023 compliance. Covers lawful basis for processing, data subject rights, cross-border transfer mechanisms, Data Protection Compliance Organisation (DPCO) registration, mandatory DPIA filing, and breach notification. Keywords: NDPR, NDPA, Nigeria, NITDA, DPCO, Africa data protection, cross-border transfer.
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。
Implements strict data minimization and retention limits for children's personal data under GDPR Art. 5(1)(c), Recital 38, UK AADC Standard 8, and COPPA Section 312.7. Covers strict necessity testing, shorter retention periods, limited profiling, parental dashboard design, and automated deletion. Keywords: data minimization, children, retention, necessity test, parental dashboard.
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 end-to-end GDPR Data Protection Impact Assessment process under Article 35, including mandatory trigger identification per Art. 35(3), DPIA content requirements per Art. 35(7), and EDPB WP248rev.01 methodology. Activate for systematic profiling, large-scale special category processing, or large-scale public monitoring. Keywords: DPIA, Article 35, impact assessment, WP248, data protection, risk assessment.
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。
优化实际模型推理链路，将正确性对齐、分段 profiling、显存与数据搬运、TensorRT/ONNX/PyTorch 后端、attention/kernel、FP8/compile、缓存与少步采样、质量回归、GPU 成本和服务验收串成同一实验闭环。当用户要求推理提速、降低显存或 GPU 成本、复现模型效果、定位 GPU 利用率低、优化图像/视频/扩散模型或自托管 LLM 时使用，提供 model inference performance optimization 的脱敏案例。纯 API 调用、提示词或 token 用量优化，以及电脑卡顿，不进入本技能的模型推理实验流程。
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 this skill to track institutional investor ownership changes and portfolio flows using 13F filings data. Analyzes hedge funds, mutual funds, and other institutional holders to identify stocks with significant smart money accumulation or distribution. Helps discover stocks before major moves by following where sophisticated investors are deploying capital.
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。
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.
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。
Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.
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。
Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
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。
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.
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。