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概要と使いどころ
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
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。
Pythonのmatplotlibでグラフ・チャートを作成するスキル。PNG/PDF/SVG出力対応。 「グラフを作って」「チャートを描いて」「可視化して」「matplotlib」等のリクエストで発動。
Searches academic literature via arXiv, Semantic Scholar, and open-access PDFs. Use when building literature reviews or finding formal research on a topic.
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。
Converts documents and URLs to markdown via tiered fallback (MCP markitdown, native tools, user notice). Use when a skill must ingest PDF, DOCX, or URL 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。
Use when you need full-text PDFs for a list of DOIs, such as a meta-analysis screening set. Batch-downloads open-access copies via Unpaywall, PMC, OpenAlex and Crossref, lists paywalled papers for manual access, and can convert PDFs to Markdown.
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 rendering a Markdown document (English or Korean) such as a proposal, IRB cover letter, handout or reference table to PDF via pandoc and xelatex, with auto-fitted table widths and CJK fonts. Not for manuscripts with a bibliography (/manage-refs) or Word forms.
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 high-quality academic Beamer slides from a thesis/paper (PDF, Word, or LaTeX source). Compatible with Claude Code and Codex. Supports thesis defense, proposal presentations, and conference talks. Built-in layout library with 13 professional page types, 5 color schemes, and interactive editing loop. Use when: (1) User asks to create thesis defense slides/PPT, (2) User wants to generate academic presentation from a paper, (3) User mentions beamer, 答辩PPT, 学术报告, 开题PPT, 论文幻灯片, or academic slides, (4) User has a PDF/docx/tex thesis and wants presentation output.
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。
全员通关后把 错题本+笔记本+知识点窗口+wiki 编译成考前速记小抄 cheatsheet.md（每条要点带可溯源 锚点），并在视觉产物模式或用户明确要求 PDF/打印版时按指定页数渲染成打印级 PDF：按「必背结论/公式 → 有难度例题（必要时含题面图）→ 例题解答（代入公式、保留基础过程）→ 要点解释（同类题怎么办）」 四段组织。当复习收尾、用户要「考前小抄/速记/总结/打印版」时使用。
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 users provide YouTube, Bilibili, or X/Twitter lecture URLs and want reader-first Chinese LaTeX/PDF notes with source-faithful claims, fluent authored prose, and verified teaching figures, especially requests phrased as lecture notes, 视频转PDF, 课程笔记, 讲义, YouTube笔记, B站笔记, X/Twitter lecture notes, or BV号; do not produce transcript dumps, quota-padded prose, or screenshot galleries.
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。
把课堂视频（本地或 B 站/YouTube）、文字稿、课件三者（任意组合）整理成一份详细的中文 Markdown 课堂笔记，输出按课程标题命名的 `{title_name}.md`（首行为 `# 文档标题`）+ 相对路径图片。Markdown 工作流，与上游 `lecture-to-notes` 的 LaTeX/PDF 输出并行存在；上游 skill 完全不动。触发词：markdown 笔记、md 笔记、视频转 markdown、Markdown 讲义、不要 LaTeX、不要 PDF、纯文本笔记。
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。
Convert structured project-note JSON manuscripts into validated EPUB and optional Kindle-clickable PDF files. Use when the user asks to turn project notes, book-system JSON, FIRE-indexed notes, Luhmann/Zettelkasten numbered chunks, table-of-contents cards, index cards, cross-linked project notes, or manuscript JSON into an EPUB/PDF with chapter directories, keyword index cards, weighted links, bidirectional backlinks, versioned filenames, chapter-splitting plans, and full link testing.
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 this skill whenever the user asks for an HTML-first visual design deliverable or design judgment: interactive prototype, slide deck, motion demo, infographic, dashboard, landing page, whitepaper, changelog, business card, social cover, brand system, design critique, multi-variant exploration, or export to MP4, GIF, PPTX, PDF, or SVG. It is optimized to make AI agents do the routing, template selection, verification, and export prep so humans spend less time prompt-engineering. Do not use for production web apps, SEO sites, backend systems, or pure copy edits.
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。
Render Markdown, HTML, or PDF paid-advertising reports from a validated Claude Ads JSON run bundle. Use for ads report, client report, audit PDF, executive audience reporting, or exporting prior audit and plan results.
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。
Parse complex documents with IBM's open-source Docling library, which turns PDFs and Office files into structured Markdown or JSON. Use when a user asks to parse a document with tables, extract figures from a document, handle multi-column layouts, convert a complex PDF to structured data, extract content from academic papers, or process documents with mixed layouts. Handles tables, figures, headers, footers, and multi-column text.
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。
Extract structured data from any document format using unified document processing. Use when a user asks to extract data from a document, parse a PDF, pull structured data from files, convert documents to JSON or CSV, extract fields from invoices or forms, or scrape data from documents.
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。
Parse architectural drawings, floor plans, and building code compliance documents using Vision AI. Extracts building type, occupancy, floor areas, room layouts, dimensions, and code parameters. Use when: reading PDF floor plans, analyzing architectural drawings, extracting building data from images or scanned documents.
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 polished HTML slide decks and PDF-ready documents for consulting deliverables. Uses the RRBC design system with warm light mode, dark mode cover pages, Lora/Inter/Roboto Mono typography, and data visualization palette. Trigger on 'deck', 'slides', 'presentation', 'pitch deck', 'keynote', 'report', or 'PDF'.
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。
Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".
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。
Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation — verdict-bearing at L1 (the LaTeX source; PDF-extracted math is unreliable, so an L0 PDF-only run surfaces info only) — never asserts 'fabricated', only that the step shown does not hold. A fresh cross-model reviewer reads the theorem/proof + an extraction-only obligation scaffold and proposes per-obligation findings, each span-anchored to the evidence ledger (claims.json); reviewer≠adjudicator. Emits proof-derivation-forensics.findings.json; NEVER computes the verdict. dimension=proof, can be critical. Triggers: "proof forensics", "check this proof", "derivation integrity", "audit the math", "证明审计", "推导有没有漏洞".
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。
End-to-end substantive-integrity forensic sweep of a research paper (especially autoresearch / AI-Scientist-style output). Orchestrates the whole pipeline: ingest (arxiv-id | pdf | dir → working dir + pdftotext for L0) → /evidence-ledger (artifact manifest + observability level L0/L1/L2 + span-anchored claims.json) → fan out the integrity auditor skills (consistency, citation, baseline, experiment, presentation, proof-derivation, eval-design — each reads the ledger, emits span-anchored findings) + the zero-verdict-weight AIS writing-style track → advisory memos (/adversarial-case-builder + /novelty-duplication-advisory, no verdict weight) → deterministic tools/adjudicate_findings.py (--ledger REQUIRED) → reviewer-ready Integrity Forensics Report. Cross-model (fresh codex per dimension) and reviewer≠adjudicator: the model proposes findings, the deterministic adjudicator decides the verdict. Observability-aware, detect-only, never an opaque AI-text classifier (a separate zero-weight AIS section lists AI writing-style impressions, never moving the verdict). Triggers: "anti-autoresearch", "integrity audit this paper", "forensic review", "audit a submission", "审一篇投稿的诚信".
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。
Convert a file from one format to another inside the container — documents, images, audio, video. Routes to the right engine (pandoc, LibreOffice, ImageMagick, ffmpeg) by format pair. Triggers on "convert X to Y", "turn this docx into a pdf", "make a gif from this mp4", "md to docx", "batch-convert these images", or any single-file or batch format change where the source and target extensions differ. NOT for editing content (use docx/pptx/xlsx/pdf skills), creating files from scratch, or reading a file you already have in context.
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。
Marp（Markdown Presentation）でプレゼンスライドを作成するスキル。PDF/PPTX/HTMLに変換可能。「スライド作って」「プレゼン資料を作って」で使用。
arXiv論文の検索・トレンド発見・詳細分析を行う統合スキル。興味度スコアでユーザーに刺さる論文を自動選定。PDF全文読み込み・Notion蓄積対応。「arXivチェックして」「論文検索」「論文分析して」で使用。
Specialized visual and multimedia processing tools. Use this skill whenever a task involves complex visual content — UI mockups, dense screenshots, design images, charts, artwork — where precise details like spacing, hex colors, font sizes, and component hierarchy need to be extracted accurately. Also use for: reviewing or auditing existing UI against designs, comparing screenshots for visual regressions, transcribing audio/video, extracting data from PDFs with complex layouts, and generating images. Trigger whenever the user wants to implement from a design, review or compare UI screenshots, analyze visual details precisely, describe artwork or aesthetic content, or process any media file (audio, video, PDF).
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。