809 件 ・ 関連度順
概要と使いどころ
Research what's genuinely new about a topic in a recent time window. Use when the user asks what's new, recent, or trending on a subject and wants a source-grounded brief rather than a quick lookup.
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。
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".
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。
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".
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 after EZ_math_model writer creates paper.md and before quality audit when a long modeling paper needs less AI-like prose while preserving formulas, data, citations, tables, and figures.
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。
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper structure".
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。
Query the project knowledge base. Wraps the research-librarian agent. The librarian reads the shelf-index, identifies the 2-4 most relevant library files for the question, deep-reads only those, and returns structured evidence with citations. Stateless — reads the index fresh on every query.
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。
Search the web and fetch page contents with Keenable AI — web search, page extraction, point-in-time queries, prompt-driven content extraction. Use when searching the web for current information, fetching webpage contents, extracting specific data from URLs, or researching topics with citations. Triggers on: web search, search the web, fetch page, read URL, find pages about, current information, Keenable, keen search, research with sources, look up, latest news, find information, check online, what is, who is.
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。
Audits websites for AI search visibility (GEO/SEO) and generates deployable artifacts (llms.txt, schema JSON-LD, robots.txt). 7-dimension scorecard tailored by site type and size, grounded in Princeton KDD 2024, Toronto 2025, and ICLR 2026 research. This skill should be used when optimizing for ChatGPT, Perplexity, Google AI Overviews, or other AI search engines. Also triggered by queries about AI search optimization, generative engine optimization, llms.txt, AI crawler configuration, schema markup for AI, citation optimization, or Google AI Mode.
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 UI component patterns across the component.gallery corpus — 60 canonical component types, ~95 production design systems, 2,600+ examples — plus an AI-era overlay (prompt inputs, tool-call cards, streaming status, citations) and composite patterns (command palette, app shell, agent console). Compare implementations, find alternative component names, ground frontend decisions in real-world precedent. Pairs with minoan-frontend-design for research-then-build workflows. Triggers on component patterns, how do others implement X, design system research, component comparison, AI chat components, agent UI patterns.
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。
Coordinate SEO, LLM-readable site architecture, UX, content, monitoring, security, and external authority work as a task-plan-driven sequence. Use this skill when the user asks to plan, execute, validate, or route multi-skill website growth work; asks what to do next across semantic core, URL structure, internal linking, schema, llms.txt, UX journeys, analytics, citation monitoring, or white-hat link placement; or wants a central architect that preserves evidence, alarms, handoffs, and 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。
Analyze credential-free public website monitoring evidence from server access logs, HTTP fetches, robots/llms/sitemap checks, and exported summaries while protecting raw IP and private query data. Use this skill when the user asks whether crawlers or AI bots reached a site, whether public SEO/LLM discovery files respond, or how to separate observed facts from unknown Search Console, rank, analytics, or citation claims.
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。
Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.
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。
Draft, expand, revise, structure, and maintain a full Islamic University of Technology BSc thesis from source documents using the local iutbscthesis LaTeX template. Use when writing or editing thesis metadata, front matter, abstract, chapters, related works, methodology, results, discussion, conclusion, citations, appendices, figures, tables, code listings, or compile guidance for main.tex. Also use when the user expects an AI agent to turn papers, notes, PDFs, LaTeX sources, bib files, data tables, and assets into a substantial thesis manuscript chapter by chapter without requiring repeated babysitting.
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 the user wants to get found in Google or AI engines (ChatGPT, Perplexity, AI Overviews) — keyword and content strategy, technical or content SEO audits, schema markup, link building, topical clustering, E-E-A-T, AI citation (AEO), or deciding whether SEO is even the right channel for their startup. Calibrated for early-stage, low-authority sites. Not for paid acquisition or channel-mix/CAC analysis (use channel-expert), or writing the page copy itself (use copywriter-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。
Create, merge, and deduplicate BibTeX bibliography files using R packages (RefManageR, bibtex). Parse .bib files into structured R objects, merge multiple bibliographies with deduplication by DOI or title similarity, generate entries from DOI/ISBN/arXiv ID, and export clean sorted .bib files. Use when creating a new .bib file for an R Markdown or Quarto project, merging bibliographies from multiple collaborators, deduplicating a .bib that has grown through copy-paste accumulation, or generating BibTeX entries programmatically from DOIs or other identifiers.
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。
Format a Quarto or R Markdown report following APA 7th edition style. Covers apaquarto/papaja packages, title page, abstracts, citations, tables, figures, and reference formatting. Use when writing an academic paper in APA format, creating a psychology or social science research report, generating reproducible manuscripts with embedded analysis, or preparing a thesis or dissertation chapter.
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 format-controlled research reports with evidence tracking, citations, and iterative review. This skill should be used when users request a research report, literature review, market or industry analysis, competitive landscape, policy or technical brief, or require a strict report template and section formatting that a single deepresearch pass cannot reliably enforce.
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。
Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete data.
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。
Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete data.
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 academic papers on arXiv, with citation counts and BibTeX.
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。
Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.
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。
根據使用者的文章或論文草稿，自動用 Semantic Scholar、Crossref、arXiv API 搜尋相關文獻，並查核既有引用(文獻是否真實存在、書目欄位是否正確、內容是否支持文中論點)，也能從主題出發寫出「每個宣稱都有真實文獻支撐」的帶引用文章；另含研究生工具組：文獻矩陣、領域地圖、research gap 偵測、閱讀筆記卡、引用完整性檢查、中英術語一致性、口試/審稿預演、新文獻追蹤、引用需求標記(annotate)、反面證據搜尋(counter)、證據強度評級(strength)、claim-evidence 總表(claims)、撤稿查詢(retract)。凡是使用者提到找文獻、找 paper、補參考文獻、查核引用、檢查 citation、驗證參考文獻、related work、literature review，貼文章要求配文獻、問「這段話有沒有文獻支持」、要求「寫一篇帶參考文獻的文章」、要整理文獻比較表、問已指名兩概念的交集有沒有人做過、要準備口試文獻答辯時，都要使用本 skill。本 skill 的 gap 指令只查證使用者已指名的 X 與 Y 交集；若使用者連題目都還沒有、需要生成並逐一淘汰候選方向，那是題目生成，不在本 skill 範圍(環境裝有 research-gap-hunter 時交給它，沒有就如實說明本 skill 只能查證已指名的交集)。本 skill 的 map 指令盤的是**文獻**——奠基文獻是哪幾篇、關鍵作者是誰、近三年往哪走；若使用者要的是「這個領域現在有哪些**做法**、每一種買到什麼付出什麼、哪些已經飽和」，那是技術盤點不是文獻盤點，交給 research-gap-hunter 的 landscape，沒有那個 skill 就如實說明本 skill 的 map 只回答文獻層的問題。
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 capturing research artifacts to a vault or knowledge base, formatting source citations, saving synthesized connections from a writing project, or enriching a knowledge base with produced content.
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。
Chinese journal scout — searches candidate journals via Wanfang API, performs L1 deterministic rejection screening (language/funding/subject mismatch), and L2 citation fingerprint analysis (reference journal distribution → academic ecosystem positioning).
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。