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概要と使いどころ
Audit research data management infrastructure -- score FAIR principles compliance (Findable, Accessible, Interoperable, Reusable) across all sub-principles, evaluate data governance frameworks and lifecycle management, assess metadata schema quality (Dublin Core, DataCite, DDI.
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Run multi-source web research with adversarial verification and produce a cited report: decomposes the question into 3-6 distinct queries (broad, specific, comparative, recency-anchored), searches and ranks sources by authority, recency, and specificity.
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Refresh Claude Ads platform, API, policy, regulation, benchmark, issue, pull-request, fork, and repository evidence. Use for ads research refresh, expired refresh_due dates, stale API or platform claims, reverify-or-demote decisions, release-current claim validation, ecosystem review, current platform changes, or updating paid-media knowledge. When tools or sources are unavailable, still demote the stale claim for the current run and block dependent release-current assertions before requesting access.
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Query a user's Gemini Notebook (formerly NotebookLM) for source-grounded answers through a logged-in browser session on their Google account. Uses the account's usage limits and writes browser state. Runs only on an explicit request: /blog notebooklm, "ask my Gemini Notebook", "ask my NotebookLM notebook", or "query my notebook library". Also used by blog-write when the user named a notebook for research. Not for generic research or note-taking.
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Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad libraries, and strategic gaps across supported platforms. Use for competitor ads, ad libraries, ad spy, competitive PPC analysis, competitor creative, Google Ads Transparency, Meta Ad Library, or paid-media competitor research.
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。
多源深度调研与专业报告生成。适用场景广泛——只要用户的问题涉及需要深度分析的专业话题，就应使用此技能。包括但不限于：(1) 用户明确要求调研/research/综述/报告/发现；(2) 用户提出一个技术或科学话题，话题复杂度需要多源深度分析；(3) 用户要求对比多种技术方案的优劣；(4) 涉及生物医药、蛋白质、基因、药物靶点等需要专业数据库支撑的问题。核心能力：根据问题性质自动组合 arXiv 论文检索 + ToolUniverse 科学工具（UniProt/OpenTargets/PubMed/FAERS 等 1000+ 工具）+ Web 搜索，多源采集数据，交叉分析，生成专业 PDF/DOCX 研究报告。
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。
Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.
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。
Turn a verified Salesforce research packet into a concise, role-aware learning brief with clear objectives, concept sequencing, worked examples, release caveats, checks for understanding, and citations mapped to claims. Trigger keywords: teach me Salesforce, explain Salesforce concept, create learning brief, Salesforce study guide, role-based lesson. NOT for open-ended source discovery or freshness verification — use architect/salesforce-learning-research first. NOT for generating production metadata or changing an org.
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。
Structured multi-hop web research with explicit confidence gating — plan the inquiry, search (WebSearch/WebFetch), score your own confidence, and keep digging until the answer is well-supported or a hop cap is hit, then emit a cited synthesis. Learns across sessions by storing each research case to memory and reusing prior strategies. Use when a question needs more than one search — comparisons, current-best-practice questions, anything where a single lookup leaves you unsure.
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Explore and debate options with fresh documentation and research before committing. Use when facing a real decision with multiple plausible approaches — library/framework choice, architecture call, API or schema design, algorithm selection, or any communication / strategy call where being wrong has cost. Enumerates relevant research domains, looks up current docs and evidence-based methods, weighs options on correctness and elegance, resists bloat. Do NOT use for divergent ideation (brainstorm), extracting user intent (elicit), or reviewing already-written code (quality-review).
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Navigator and help guide for the UniKit framework — use when the user is unsure what to do next, where to start, or which unikit skill fits their situation. It diagnoses intent and points to the right skill or pipeline: starting a project, writing code, testing, planning, the spec-driven code workflow, authoring or detailing game design, finding a new mechanic, or deciding what game to build. Trigger on questions like "what do I do next", "where do I start", "how do I start coding", "how do I test my code", "how do I create the game design", "how do I detail a system", "how do I find a new mechanic", "which unikit skill should I use", "how does the code pipeline relate to game design", "I'm lost in unikit", "help me with unikit". Read-only — it explains and points to the right command, it never edits project files. For technical code research use /unikit-explore; for game-design research use /unikit-gd-explore; to initialize the framework use /unikit.
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。
Curate the project's knowledge base and memory in .unikit/memory/ — the indexed reference library of coding standards and framework usage patterns. ALWAYS use this skill whenever the user adds knowledge from a source: a URL, a file, a folder path, a PDF, an article, or a book — any path/link/document is a strong signal for the knowledge base, not a quick rule. Also use it to research or document how the project uses a framework, or to add a researched core/stack knowledge entry. Sub-commands by phrasing: "migrate the rules" or "promote rules" runs migrate-rules; "optimize the knowledge base" or "optimize the rules" runs optimise (extract large sections into references); "validate" re-syncs RULES_INDEX.md; "--module <id>" targets a module. For a quick one-line convention or correction typed as a prompt ("always do X", "never use Y", "remember this") with no source, use /unikit-rules; for architecture decisions use ARCHITECTURE.md.
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。
Market researcher AI — survey, focus group, persona, JTBD, competitive 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。
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。
Competitive benchmarks, solution research, and hypothesis generation for identified bottlenecks
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。
MUST USE when user wants to research/search/look up/find anything on the internet — e.g. "research this topic", "do a deep dive on X", "search the web for X", "see what people say about X", "look this up". Also MUST USE when user mentions any platform or shares any URL/link: Twitter/X, Reddit, Facebook, Instagram, YouTube, GitHub, Bilibili, XiaoHongShu, Xiaoyuzhou Podcast, LinkedIn/jobs/recruiting, V2EX, Xueqiu (stocks), RSS. 15 platforms, multi-backend routing (OpenCLI / per-platform CLIs / APIs). Zero config for 6 channels. Run `agent-reach doctor --json` to see which backend serves each platform right now. NOT for: writing reports/analysis/translation (this skill only FETCHES internet content); posting/commenting/liking (write operations); platforms that already have a dedicated skill installed (prefer that skill).
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 and normalize MotionSites-style prompt directions, then output a clean motion prompt brief for implementation.
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Discover which AI prompts and topics matter for a brand's Answer Engine Optimization (AEO) using only free tools. Crawls a website, analyzes the brand's positioning, generates prioritized prompts people ask AI assistants, and audits existing content coverage — all without paid APIs. Use when a user wants to: find what questions people ask AI about their industry, discover AEO opportunities, research prompts for content creation, audit a site's AI visibility, or build an AEO content strategy. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only.
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 or refresh AEO-optimized content that gets cited by AI assistants (Gemini, ChatGPT, Perplexity) using only free tools. Two modes: CREATE new content targeting a specific prompt, or REFRESH existing content to improve AI citation-worthiness. Researches what AI models currently cite, builds a competitive brief, and produces citation-worthy content. Use when a user wants to: write content optimized for AI citations, create articles that show up in AI answers, refresh/update existing content for better AI visibility, build authority content for answer engines, or produce AEO content without paid tools. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only. Pairs with aeo-prompt-research-free (which identifies WHAT to write about; this skill handles HOW to write or refresh it).
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。
Find question-based Google Autocomplete suggestions for any topic. Prepends question modifiers (what, how, why) to a seed topic and returns real autocomplete suggestions — useful for AEO prompt research, content ideation, and understanding what people ask about a topic. Use when the user wants to discover questions people search for, find content angles, or do keyword/prompt research for a topic.
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Add real references and standardize citations for research papers and theses (为科研论文和毕业论文添加真实参考文献并规范引用标注)
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Refine academic writing for computer science research papers targeting top-tier venues (NeurIPS, ICLR, ICML, AAAI, IJCAI, ACL, EMNLP, NAACL, CVPR, WWW, KDD, SIGIR, CIKM, and similar). Use this skill whenever a user asks to improve, polish, refine, edit, or proofread academic or research writing — including paper drafts, abstracts, introductions, related work sections, methodology descriptions, experiment write-ups, or conclusion sections. Also trigger when users paste LaTeX content and ask for writing help, mention "camera-ready", "rebuttal", "paper revision", or reference any academic venue or conference. This skill handles both full paper refinement and section-by-section editing.
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。
The academic research platform for AI agents. Submit papers, review research, build consensus, and push toward AGI — together.
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。