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概要と使いどころ
Cinematic and game lighting in Blender including three-point lighting, HDRI, horror lighting, volumetrics, rim lighting, and mood-driven illumination. Use for look development and scene atmosphere via MCP.
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Art direction for survival and survival-horror gameplay spaces in Blender emphasizing resource props, crafting readability, and tension routes.
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。
Godot-specific Blender export favoring GLTF/GLB with correct scale, materials, animation clips, and collision shapes via MCP.
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。
Character archetype art direction for Blender covering fantasy races, horror creatures, roles (knight, assassin, detective), and body types (mecha, android, chibi, etc.).
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 introducing or correcting grouped Spring Boot configuration, typed property binding, validation, profiles or secret injection. Do not rewrite unrelated single-value configuration.
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。
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
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。
Validates whether an API request is correct based on provided inputs (method, URL, headers, body, auth, query params).
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。
Create, revise, or audit AGENTS.md files from repository evidence, verified commands, and correctly scoped instructions without overwriting maintainer intent.
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。
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
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。
New Jersey Data Privacy Act (NJDPA) compliance, effective January 15, 2025. Covers consumer rights (access, correction, deletion, portability, opt-out), controller obligations, sensitive data requirements, universal opt-out mechanism recognition, 30-day cure period (sunsets after 18 months), and AG enforcement. Keywords: NJDPA, New Jersey, data privacy, consumer rights, sensitive data, universal opt-out, AG enforcement.
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Guides determination of the correct lawful basis under GDPR Article 6(1)(a)-(f) for each processing activity. Includes decision tree logic for consent vs legitimate interest vs contract necessity. Activate when evaluating legal grounds for processing or reviewing lawful basis selections. Keywords: lawful basis, Article 6, consent, legitimate interest, legal obligation, contract.
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。
Delaware Personal Data Privacy Act (DPPA) compliance implementation. Covers consumer rights (access, correct, delete, portability, opt-out), controller obligations, processor requirements, sensitive data consent, universal opt-out recognition, DPIA requirements, and AG enforcement. Effective January 1, 2025, with no revenue threshold for applicability.
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。
California consumer privacy rights workflow implementation under CCPA/CPRA. Covers right to know, delete, opt-out of sale/sharing, correct, and limit sensitive PI processing. Includes 45-day response timelines, identity verification procedures, and authorized agent handling.
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Create professional, consultant-grade PowerPoint presentations from scratch using MckEngine (python-pptx wrapper) with McKinsey-style design. Use when user asks to create slides, pitch decks, business presentations, strategy decks, quarterly reviews, board meeting slides, or any professional PPTX. AI calls eng.cover(), eng.donut(), eng.timeline() etc — 67 high-level methods across 12 categories (structure, data, framework, comparison, narrative, timeline, team, charts, images, advanced viz, dashboards, visual storytelling), consistent typography, zero file-corruption issues, BLOCK_ARC native shapes for circular charts (donut, pie, gauge), production-hardened guard rails for spacing, overflow, legend consistency, title style uniformity, dynamic sizing for variable-count layouts, horizontal item overflow protection, chart rendering, and AI-generated cover images via Tencent Hunyuan 2.0 with professional cutout, cool grey-blue tint, and McKinsey-style Bézier ribbon decoration.
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Guide Claude to use SigCLI correctly — check auth, login, get credentials, configure providers, and onboard new websites. Trigger when using sig commands, editing ~/.sig/config.yaml, or needing authenticated API access.
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。
Create original short videos using the 正面撤退｜通道坍缩逼向唯一庇护 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.
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。
Create a Seedance 2.0 prompt for a rough hand-drawn luminous entity that physically contacts a live-action space, continuously transforms while remaining traceable, escapes through adjacent geography, and is followed by a slightly delayed handheld camera. Use for playful 4–15 second mixed-media clips; not for horror, polished CG, unrelated cuts, or simple visual filters.
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。
Provides React patterns for hooks, effects, refs, and component design. Covers escape hatches, anti-patterns, and correct effect usage. Use when reading or writing React components with hooks or effects.
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Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.
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Use when analyzing failures, outages, incidents, or negative outcomes, conducting blameless postmortems, documenting root causes with 5 Whys or fishbone diagrams, identifying corrective actions with owners and timelines, learning from near-misses, establishing prevention strategies, or when user mentions postmortem, incident review, failure analysis, RCA, lessons learned, or after-action review.
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Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation. Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents. Supports AI model specs, technical documentation, statistics, and general factual statements.
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。
Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.
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。
Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.
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。