調査、論文、教育、資格学習（4,124 件）
概要と使いどころ
Use when writing CS research papers (conference, journal, thesis), reviewing scientific manuscripts, improving academic writing clarity, or preparing IEEE/ACM submissions. Invoke when user mentions paper, manuscript, research writing, journal submission, or needs help with academic structure, formatting, or revision.
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。
When the user requests to add academic papers to a BibTeX bibliography file while maintaining format consistency and sourcing from appropriate repositories. This skill handles 1) Reading existing BibTeX files to understand formatting conventions, 2) Searching for academic papers across multiple sources (OpenReview for conference papers, arXiv for preprints), 3) Extracting proper BibTeX metadata from conference pages or arXiv entries, 4) Determining appropriate citation format (@article vs @inproceedings) based on publication venue, 5) Appending new entries while preserving existing file structure and formatting. Triggers include requests to 'add to ref.bib', 'update bibliography', 'cite papers', or when working with academic reference files.
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。
Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.
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。
Academic paper search across 14+ scholarly platforms including arXiv, PubMed, Google Scholar, Web of Science, Semantic Scholar, Sci-Hub, and more. Use for literature review, research discovery, and citation management.
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。
Complete workflow for topology optimization using Tosca. Use to minimize weight while maintaining stiffness. Requires full Abaqus license (not Learning Edition).
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。
[73] CLOSE. Record and maintain Lessons in MEMORY.md after a problem is solved or the user confirms success. Use when capturing a new lesson, moving lessons through the pipeline, or enhancing Project Architecture Quick Reference with new 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。
[00] META. Orchestrate skills into dynamic chains for complex tasks. Analyzes the task, discovers available skills, builds an optimal chain, explains why each skill is needed, and executes step-by-step with user confirmation. Use for any complex task requiring multiple thinking/research/analysis steps. Triggers on "plan this", "how to approach", "what's the strategy", "build a plan", or any multi-step problem requiring skill orchestration.
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。
[22] UNDERSTAND. Deep research from all sources — internal (code, git, logs) AND external (web, docs, best practices). Use when choosing libraries, investigating solutions, understanding legal/technical questions, comparing approaches, or anytime you need comprehensive knowledge from both project context and world knowledge. Triggers on "research", "investigate", "find best approach", "what do others do", "compare options", or complex questions requiring multiple sources.
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。
Executive coaching skill that helps you plan your 2026 using research-backed process goals. Guides you through discovery questions, creates outcome goals, converts them to daily behaviors, and sets up accountability systems. Use when you want to plan your year, set goals, or need a coach to help you stay focused.
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。
[21] UNDERSTAND. Deep research workflow for this project using 150% scope (100% core + 50% boundary), evidence-based reasoning, and structured investigation notes. Use when the task requires investigation, root-cause analysis, or mapping unknown areas. Always maintain a research log file that captures findings, hypotheses, and next branches; use web.run when external verification is needed.
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。
status:needs-investigation な issue を並列調査し、結果をコメントして status:investigated に更新する
status:investigated な issue の調査結果コメントを読み、実装可能なら status:ready に昇格させる
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
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。
Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.
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 extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
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。
Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.
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。
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
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。
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
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。
/coach-me と入力されたときだけ起動する、Learning by Doing コーチングスキル。現在の会話を分析し、Anthropicの4D AIフルーエンシーフレームワーク（Delegation / Description / Discernment / Diligence）に基づいて「うまくできていた点」「改善できる点」「次回に使えるプロンプト例」をフィードバックする。オプションで /coach-me 詳細 または /coach-me --detail と入力すると次元ごとの深掘りフィードバックを提供。自動起動は一切しない。ユーザーが /coach-me と明示的に入力した場合のみ使用すること。
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
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 market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
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。
Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo.
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。
Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
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 complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
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。