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概要と使いどころ
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
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Scene composition and orchestration skill for creating complex multi-part animations. **Triggers when:** - User wants to compose multi-element scenes - Request involves combining multiple visual elements - Scene requires act-based structure or transitions - User mentions "scene", "compose", "combine", "orchestrate" **Capabilities:** - Scene graph construction with acts and transitions - Multi-object coordination and timing - Camera movements and focus control - Layered animation sequences
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。
Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.
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 coordinating complex tasks across multiple AI agents with a centralized handoff document for planning, execution tracking, and feedback fusion.
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 creating elaborate, multi-component claude.ai HTML artifacts with React, Tailwind CSS, shadcn/ui, state management, routing, or complex frontend structure; not for simple single-file HTML/JSX artifacts.
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 Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.
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, implement, audit, or refactor web and game-adjacent interfaces using an evidence-based Hypergryph visual language with two independent choices: product family and application depth (1 minimal, 2 moderate, 3 complex, 4 maximal). Use for Hypergryph-, Arknights-, Endfield-, Rhodes Island-, industrial sci-fi-, editorial game-UI-, or bilingual technical-interface requests; for landing pages, dashboards, menus, HUD-like panels, design systems, HTML/CSS/JS/React components, and visual QA where the result should carry this family resemblance without copying protected logos or artwork.
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。
Perform the repository's risk-tiered independent final review before implementation completion. Use only when explicitly invoked or when repository instructions require it after behavior-impacting implementation work; audit the complete task diff, supported contracts, lifecycle and security boundaries, complexity, and tests before final verification.
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 BIRD Book Deconstructor 2.2 to apply the formal BIRD 2.1 Knowledge Address protocol to complex manuscript text and TheBrain Thoughts. Use when splitting books into chapter/section/item knowledge nodes, assigning Book Address, structured Knowledge Index (Weight, Type, Keyword, Alias), Routes, verified Deep Links, and Semantic Roles; producing BIRD Excel workbooks, TheBrain scaffolds, Roam JSON, monochrome printable double nine-grid cards, or a routed handoff from iMandalArt to A4 eight-page booklets; or auditing and migrating existing BIRD/TheBrain indexes.
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 reduces bug-triggering test cases to minimal form while preserving the failure. Use when debugging with large, complex test cases that need simplification, reproducing bugs with minimal examples, or creating regression tests from verbose failure scenarios. Takes a failing test and systematically removes unnecessary inputs, steps, assertions, and code using delta debugging and other reduction algorithms. Supports unit tests, integration tests, input files, and multiple programming languages (Python, JavaScript, Java, C/C++).
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Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.
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。
Interview-based strategic planning for complex software tasks. Conducts structured requirements gathering, gap analysis, and generates detailed work plans before implementation begins.
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。
Creates visual representations of system structure including architecture diagrams, data flow diagrams, deployment diagrams, and sequence diagrams. Use when Claude needs to visualize system components, infrastructure, data flows, or interactions. Supports Mermaid (recommended for Markdown/GitHub), PlantUML (for detailed UML), and Graphviz/DOT (for complex networks). Trigger when users request diagrams, visualizations, or ask to "show", "diagram", "visualize", or "map out" system architecture, infrastructure, data flows, or component interactions.
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 a repository and its test suite to generate customized mutation operators tailored to the project. Use this skill when setting up mutation testing, improving test quality, or generating project-specific mutants. Considers language constructs, business logic patterns, API calls, data types, and code complexity to produce effective mutation operators that maximize test sensitivity while avoiding trivial or equivalent mutations. Generates prioritized operator sets (high/medium/low value) with impact summaries. Triggers when users ask to generate mutation operators, customize mutation testing, analyze code for mutation opportunities, or create project-specific mutants.
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。
Summarize long Isabelle or Coq proof scripts into high-level logical steps and reasoning flow. Use when users need to: (1) Understand the structure of a complex proof, (2) Document proof strategies for others, (3) Extract the key reasoning steps from verbose proof scripts, (4) Create readable proof outlines from detailed tactical proofs. Produces hierarchical outlines with moderate detail showing proof structure, main cases, key lemmas, and reasoning flow for both Isabelle/Isar and Coq proofs.
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。
Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.
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。
Analyze failed or stuck proofs and propose auxiliary lemmas to help complete the proof in Isabelle/HOL or Coq. Use when encountering proof failures, stuck proof states, unprovable subgoals, or when needing to strengthen induction hypotheses. Identifies missing lemmas, suggests proof strategies, and generates helper lemmas with appropriate statements and proof sketches. Supports inductive proofs, case analysis, rewriting, and complex proof obligations.
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。
Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.
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 meaningful comments and documentation for code to improve maintenance and readability. Use when adding documentation to Python or Java code, including function/method docstrings, class documentation, inline explanations for complex logic, and code annotations (TODO, FIXME). Analyzes existing comment style in the codebase to match conventions. Produces clear, concise comments that explain the "why" not just the "what", following best practices for each 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。
Suggest and apply code refactorings to improve readability, maintainability, and code quality. Use this skill when improving existing code structure, eliminating code smells, applying design patterns, simplifying complex logic, extracting duplicated code, renaming for clarity, or preparing code for new features. Provides specific before/after examples, explains benefits, identifies risks, and ensures behavior preservation through tests.
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 and optimizes code for better performance, memory usage, and efficiency. Use when code is slow, memory-intensive, or inefficient. Supports Python and Java optimization including execution speed improvements, memory reduction, database query optimization, and I/O efficiency. Provides before/after examples with detailed explanations of why optimizations work, complexity analysis, and measurable performance 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。
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
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。
Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean.
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。
Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted 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。