2,123 件 ・ 関連度順
概要と使いどころ
Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories.
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 deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML/SVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, plots, axes/scales, legends, tooltips, or data-driven SVG output.
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。
Nonlinear optimization with CasADi and IPOPT solver. Use when building and solving NLP problems: defining symbolic variables, adding nonlinear constraints, setting solver options, handling multiple initializations, and extracting solutions. Covers power systems optimization patterns including per-unit scaling and complex number formulations.
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。
Combine visual features (face detection, lip movement analysis) with audio features to improve speaker diarization accuracy in video files. Use OpenCV for face detection and lip movement tracking, then fuse visual cues with audio-based speaker embeddings. Essential when processing video files with multiple visible speakers or when audio-only diarization needs visual validation.
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。
Compare multiple solutions for correctness and 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。
Set up cosmiconfig for hierarchical configuration loading from multiple sources and formats.
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。
Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking
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。
Batch embedding generation with caching, rate limiting, and multiple provider support
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。
Design agents for multi-turn tool use — sequential tool calls, result accumulation, error recovery, and complex task decomposition over multiple turns.
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。
Orchestrate workflows across multiple applications and APIs — inter-app coordination, data handoff, and multi-system task completion.
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 for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Multiple LLM reviewers challenge changes from independent angles.
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。
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
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。
Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.
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 ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.
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。
Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.
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。
This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics.
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Monitor portfolio risk, R-multiples, and position limits. Creates hedging strategies, calculates expectancy, and implements stop-losses. Use PROACTIVELY for risk assessment, trade tracking, or portfolio protection.
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。
Analyzes multiple provided pages to identify keyword overlap and potential cannibalization issues. Suggests differentiation strategies. Use PROACTIVELY when reviewing similar content.
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。
Static Application Security Testing (SAST) for code vulnerability analysis across multiple languages and frameworks
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。
Master Julia 1.10+ with modern features, performance optimization, multiple dispatch, and production-ready practices. Expert in the Julia ecosystem including package management, scientific computing, and high-performance numerical code. Use PROACTIVELY for Julia development, optimization, or advanced Julia patterns.
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Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
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Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs (DiD/event-study, IV, panel). Use when user says "power analysis", "power calculation", "MDE", "minimum detectable effect", "how big a sample do I need", "is my study powered", "power for an RCT", or when /preregister needs a power section for an experiment. Produces a power/MDE table, power curves, and a methods paragraph to paste into a preregistration.
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Use when creating Codex, Claude Code, Gemini CLI, Cursor, or AGENTS.md runtime adapters from one canonical agent core. Use whenever a generated repo needs multiple AI runtimes without duplicating instructions.
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Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.
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。