3,790 件 ・ 関連度順
Gets people to actually use what was delivered — stakeholder analysis, communication, training, resistance, and measuring adoption. Use this to plan a rollout, recover an implementation nobody is using, handle resistance to a change, sequence communications, or work out why a technically successful project changed nothing.
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Search file contents using regex pattern across directories. Cross-platform pure Python implementation (no external tools needed). Returns matching lines with file paths and line numbers.
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You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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Use when implementing any feature or bugfix, before writing implementation code
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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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。
Designs primary, secondary, and exploratory endpoints for biomedical and clinical research protocols. Always use this skill when a user needs to translate study aims into operational endpoint definitions with event rules, assessment timing, composite logic, interpretability, and protocol-stage auditability. Focus on endpoint precision, feasibility, clinical meaning, ambiguity reduction, and implementation readiness rather than generic study design advice.
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Detects methodological gaps across study design, analysis, validation, bias control, reproducibility, and implementation readiness within a biomedical research area. Use this skill when a user wants to identify what current studies are still methodologically missing, which weaknesses are most consequential, and what upgrade path would produce a stronger next-step study. Always separate design gaps, analysis gaps, validation gaps, and reproducibility gaps. Never treat technical complexity as methodological rigor.
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Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from generic statements of importance.
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Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.
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。
SkillsBench task authoring — walk a contributor from idea to submission-ready task following CONTRIBUTING.md and the task-implementation rubric. Use when the user wants to create a new SkillsBench task, scaffold a task from an existing workflow (notebook, Excel workbook, document, dataset), convert a prompt or a benchmark item into a SkillsBench task, write skills for a task, or prepare a SkillsBench PR. Pairs with `task-review` (run that as a self-check before submitting).
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。
Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.
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Comprehensive security engineering skill for application security, penetration testing, security architecture, and compliance auditing. Includes security assessment tools, threat modeling, crypto implementation, and security automation. Use when designing security architecture, conducting penetration tests, implementing cryptography, or performing security audits.
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World-class Java and Spring Boot development skill for enterprise applications, microservices, and cloud-native systems. Expertise in Spring Framework, Spring Boot 3.x, Spring Cloud, JPA/Hibernate, and reactive programming with WebFlux. Includes project scaffolding, dependency management, security implementation, and performance optimization.
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。
Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shards), modern optimizers (Muon, AdamW), mixed precision training, and training loop implementation.
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Bootstrap MCP (Model Context Protocol) servers with the official TypeScript SDK. Creates complete server implementations with transport layer, tools, resources, and proper error handling.
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Provide number theory algorithm implementations and guidance
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Generate customized segment tree implementations
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Provide implementations of advanced data structures
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Use when executing implementation plans with independent tasks in the current session. Dispatches fresh subagent per task.
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Design technical architecture, select technology stack, and define implementation strategy from specifications and constitution constraints.
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LangChain ReAct agent implementation with tool binding for reasoning and action loops
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Use when receiving code review feedback, before implementing suggestions. Requires technical rigor and verification, not blind implementation.
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。
Execute development tasks to build features, producing code, tests, and configuration artifacts that satisfy specification requirements and comply with constitution standards.
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。
LangChain retriever implementation with various retrieval strategies for RAG applications
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。