調査、論文、教育、資格学習（4,203 件）
概要と使いどころ
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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User research methods, customer insight gathering, and problem validation for product discovery.
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。
Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis
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 EMERGING trends by connecting dots across unrelated sources. Monitor niche communities, academic research, GitHub, patents, funding, regulatory changes. Predict what will trend in 3-6 months based on weak signals.
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-first content creation optimized for both human readers and AI search engines (Claude, ChatGPT, Perplexity, Gemini). Creates authentic, authoritative content that becomes the go-to citation source for AI models answering user questions. Use this skill when: - Creating content that should appear in AI search results (Perplexity, ChatGPT, Claude) - Building topical authority to become THE source AI cites for a topic - Launching a new product and need compelling, citable content - Creating blog posts, articles, social media, or press releases - Need content that references real trends, people, and recent events - Want AI-assisted content that doesn't sound AI-generated - Creating thought leadership content in any industry Triggers: "create content for", "write about", "research and write", "find experts for", "content for launch", "blog post about", "article on", "press release for", "AI search", "show up in AI", "Perplexity", "be cited by AI"
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。
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
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 a self-contained, beautifully styled HTML analysis of an academic paper. Use when the user provides an arXiv link, paper URL, PDF, or asks to analyze a research paper. Produces a standalone HTML file with structured sections (Problem, Translation/Analogy, Architecture, Key Results, Verdict), embedded figures, and responsive design. Trigger words include 读论文, 分析论文, paper analysis, paper 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。
Conduct deep web research using the openbrowser-ai agent: decompose a query, investigate sub-questions across multiple sources, and produce a cited markdown report plus structured JSON under local_docs/research/. Trigger when the user asks to: research a topic, do a deep dive, investigate, gather evidence, compare options, write a literature review, build a briefing, or produce a cited report.
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。
搜索微信公众号文章技能。通过微信搜索获取文章列表，覆盖科技/AI、社会热点、财经、教育、职场等各类中文资讯；可按关键词检索并返回标题、概要、发布时间、来源公众号与链接。当用户需要查找微信公众号文章、整理参考资料或快速获取文章信息时使用此技能。
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 reviewing what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.codex/journal
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 starting work in any divine-* repo (divine-web, divine-mobile, divine-funnelcake, keycast, divine-blossom, divine-router, etc.) or when you've learned a cross-repo fact worth sharing back. The shared divine-context handbook at ~/code/divine/divine-context/ is the source of truth for cross-cutting architecture, Nostr usage, terminology, and the project catalog across the ~50 divine-* repos.
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 reviewing what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.claude/journal
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。
How TestDriver learns your app and caches what it discovers for instant, deterministic replays
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。
Full-stack autonomous math modeling agent for MCM/ICM (美国大学生数学建模竞赛). Handles team control number, problem choice (A–F), English academic writing, mcmthesis LaTeX template, and optional practical deliverable (memo/letter/report). Use when the user provides an MCM/ICM problem and wants end-to-end automated modeling, coding, and paper generation in English.
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 proof states in Isabelle or Coq and suggest applicable tactics to make progress. Use when users need help with: (1) Choosing the next tactic in an interactive proof, (2) Understanding what tactics apply to their current goal, (3) Getting unstuck in a proof, (4) Learning which tactics work for specific goal structures (conjunctions, implications, induction, etc.). Provides 3-5 ranked tactic suggestions with explanations for intermediate-level proofs in both Isabelle/Isar and Coq.
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。
Synthesize user research findings from interviews, surveys, and analytics. Create insight reports, customer journey maps, and actionable recommendations based on research data and qualitative findings.
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Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer. Use when a customer asks a question you need to investigate, when building background on a customer situation, or when you need account context.
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Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect enrichment tools or your CRM. Trigger with "research [company]", "look up [person]", "intel on [prospect]", "who is [name] at [company]", or "tell me about [company]".
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 a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.
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 a specific person using Common Room data. Triggers on 'who is [name]', 'look up [email]', 'research [contact]', 'is [name] a warm lead', or any contact-level question.
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。
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
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Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.
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。
Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.
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。
Conduct game domain and industry research. Use when the user says "lets create a research report on [game domain or industry]"
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。