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概要と使いどころ
Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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 integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI 2.0 ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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。
Conducts Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific health privacy regulations. Covers special category data safeguards, clinical research data, patient portals, health wearables, genetic data, and cross-border health data transfers. Keywords: health data PIA, DPIA, Article 9, HIPAA, special category data, clinical research, patient privacy, genetic data.
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Implements HIPAA Privacy Rule requirements under 45 CFR §164.500-534 for covered entities and business associates. Covers minimum necessary standard, treatment-payment-operations exceptions, directory opt-out, personal representative rules, and authorization requirements. Keywords: HIPAA Privacy Rule, PHI, minimum necessary, TPO, authorization, covered entity.
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Guides EU Code of Conduct adherence under GDPR Articles 40-41 including EDPB approval requirements, monitoring body accreditation, code drafting, adherence declaration, compliance verification, and complaint handling. Covers sector-specific codes, transnational codes, and Art. 40(3) approval by supervisory authorities. Keywords: code of conduct, Article 40, Article 41, EDPB, monitoring body, adherence.
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Guides development of GDPR Article 40-41 codes of conduct for industry sectors including drafting, submission, and monitoring body requirements. Activate when creating industry codes or establishing monitoring bodies. Keywords: codes of conduct, Article 40, Article 41, monitoring body, industry code.
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Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.
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。
Produce a standalone test plan by analyzing code for test coverage gaps and edge cases. Use when you need to create, generate, or draft a test plan for a branch, need to analyze test coverage, or need to identify what tests to write for specific files or directories. Does not produce a plain-language plan for a person to run tests by hand — use manual-test-planning for that. Does not write test code — use tdd to implement behavior test-first. Does not refine existing plans — use iterative-plan-review. Does not review code quality, security, or style — use code-review for full code review. Does not evaluate architectural testability or structural coupling — use architectural-analysis for architectural 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。
Analyzes an existing codebase using strategic Domain-Driven Design to discover bounded contexts, surface ubiquitous language and semantic collisions, find places where code boundaries diverge from domain boundaries, and produce an evidence-backed domain and context map. Use when the goal is DDD-specific: bounded context discovery, ubiquitous language analysis, domain model discovery, or mapping contested ownership and responsibility. The entire repository is a valid scope; no module or directory must be named first. Does not assess architectural coupling, cohesion, or technical debt — use architectural-analysis. Does not design service communication, integration patterns, or deployment topology — use architectural-analysis or plan-a-feature. Does not refactor code, diagnose bugs, or review code quality — use refactor, investigate, or code-review.
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。
Produces a human-readable, progressive-disclosure overview of unfamiliar code or a pull request's changes — why it exists (the real problem it solves or goal it serves for the business or a user), and from there what it does, how it flows, and where to start — so you can get up to speed before working on or reviewing it. Use when you want to understand, get oriented in, make sense of, explain, or get up to speed on a chunk of code, a file, a directory, a symbol, or a PR's changes. Writes the overview to a scratch file and changes no code. Does not review code quality or raise findings — use code-review for auditing changes or post-code-review-to-pr for posting them. Does not produce durable feature or system documentation — use project-documentation. Does not assess architecture or structural risk — use architectural-analysis. Does not diagnose bugs or root-cause failures — use investigate. Does not pace a person through the code one step at a time in conversation — use code-walkthrough.
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。
Walks a person through code changes one step at a time in conversation, starting at the entry point and following the flow that changes, showing a small chunk per step and explaining it in plain language. Defaults to the current branch's changes, and walks the code from the perspective of any context provided instead — a file, directory, symbol, pull request, plan, or ticket. Use when someone wants to be walked through, taught, paced through, or shown around code or a branch step by step, or to learn how a change works before reviewing or extending it. Stops after every step and waits, so the learner sets the pace. Paces through code that already exists and builds nothing — to build new work while being paced through it, use pairing. Does not produce a written overview to read alone — use code-overview. Does not review code quality — use code-review. Does not diagnose bugs — use investigate.
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 互动图鉴｜固定菜单切换生物 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 original short videos using the 护送穿行｜关系确认到发光门槛 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.
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Design a Seedance 2.0 science, education, or general-knowledge explainer using layered handmade papercraft, miniature diorama depth, physical paper mechanisms, clear knowledge beats, restrained miniature camera, tactile sound, and stable reference roles. Use for one 4–15 second clip or a longer multi-clip plan; not for flat vector cartoons, live action, or paper-texture-only styling.
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 relaxed black-pen graphics from concepts or visual references, including sparse illustrations, narrative scenes, animals, objects, abstract relationships, icons, and logo marks. Use when the user wants quick hand-drawn line energy, causal story staging, non-equilibrium proportion, shorthand forms, open contours, structural line drift, misregistered junctions, selective retracing, physical pen texture, or a transferable illustration system that must not collapse into clean vector minimalism or polished period-sketch drawing.
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。
Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.
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。
Generate or update repository-specific AGENTS.md instruction files from real repo evidence. Use when asked to create, design, scaffold, split, or improve root or scoped AGENTS.md files for Codex/Claude/agent workflows, especially when a repo needs directory-specific rules, validation commands, generated-file boundaries, or a short agent onboarding router.
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, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.
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Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.
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 mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.
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。
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
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。
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.
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 complete Claude Code workflow directories with curated skills. Use when user wants to (1) create a new workflow for specific use case (media creator, developer, marketer, etc.), (2) set up a Claude Code project with pre-configured skills, (3) download and organize skills from GitHub repositories, or (4) generate README.md and AGENTS.md documentation for workflows. Triggers on phrases like "create workflow", "new workflow", "set up workflow", "build a xxx-workflow".
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。
Inject Call-to-Action blocks into Astro site content with intelligent placement strategies. Use when the user wants to add CTAs, newsletter signups, product promotions, or any content blocks to blog posts. Supports multiple placement strategies (end, after 50%, after 60%), content scoring for relevance, and dry-run preview.
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。