<What it does, one line>. Trigger phrases: <phrases>.
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 an academic paper (PDF, LaTeX, pasted text, thesis chapter or technical disclosure) into a Chinese invention patent application draft — 说明书摘要、摘要附图、权利要求书、说明书、说明书附图 — delivered as DOCX/PDF with black-and-white patent drawings, plus a separate drafting-notes file covering novelty/disclosure risk, claim layout and claim-to-paper support. Use whenever the user wants to 论文转专利, 写专利/专利申请书/专利交底书, draft or review 权利要求书, write a 说明书, make 专利附图, or check a patent draft against its source paper, even if they only say "帮我把这篇论文写成专利".
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Convert a paper into a Chinese invention patent application draft that a patent attorney (专利代理师) can review and file with minimal rework. The output is a draft, not legal advice; say so once in the delivery message.
A strong draft does four things the paper itself does not:
【待补充:…】 placeholder, never a guess — added matter cannot be supported later and misrepresents the inventors' work.direct (default: draft everything, mark gaps), human-in-loop (confirm disclosure status, invention points and claim plan with the user before drafting), text-only (no files), claims-only, or review (check an existing draft against the paper).references/input-requirements.md for PDF handling and the minimum input.references/patentability-and-disclosure.md — every time, because the answer changes what you tell the user.Before drafting, list the technical facts you will rely on, each with its location (§3.2, Eq. (4), Fig. 2, Table 3, Appendix B). Separate: (a) the paper's own contributions, (b) prior art the paper describes (related work, baselines), (c) experimental settings and results. This list is the backbone for fidelity, for support_map, and for the 背景技术 section. Do not show it to the user unless asked.
Identify the closest prior art (usually the strongest baseline or the method the paper improves on), the distinguishing features, the technical problem they solve and the technical effect they produce. Then plan the claim tree before writing any claim: triage every candidate claim-1 feature (necessary / embodiment choice / interface detail), give each separable mechanism its own dependent claim (one fallback position per claim, never merged to save numbering), check that every branch of a generalised claim 1 is supported in the description, add the standard carrier claims, and record all of this in notes.claim_plan. Do not simply transcribe the paper's pipeline into claim 1. Read references/claims-drafting.md.
Follow references/patent-drafting-standard.md for the structure, content and language of each part, including the paper-to-patent mapping (what to keep, transform, or drop). Write in Simplified Chinese patent register: 本发明/本实施例, never 本文/我们/论文/作者. Keep technical clarifications (what a constraint or state actually means) in the text; move drafting remarks to the notes.
Then run the sufficiency audit (patent-drafting-standard.md §7): for every feature of claim 1 and the main dependent claims, check that the description states its data format, decision rule, failure handling and parameters. What the paper gives goes into the text; what it does not give goes into notes.sufficiency as a gap — never filled in by you. This audit is what produces a useful handover list; a draft that looks complete but hides its gaps is worse than one that names them.
Design each figure as an explicit graph of nodes and edges taken from the paper's figures and method text. If the method checks, revises, retries or iterates, draw that loop (decision node + return edge) in the flowchart that illustrates claim 1, not just the success path. Read references/drawing-generation.md. A typical AI-method set: method flowchart (图1, also the 摘要附图), model/data-flow diagram redrawn from the paper's main figure, apparatus block diagram with reference numerals, electronic-device diagram.
Write the structured JSON described in references/document-generation.md (see assets/example_patent_content.json for a complete example). Then run, from the folder holding the JSON (<skill> = this skill's directory):
python <skill>/scripts/check_patent_draft.py patent.json
python <skill>/scripts/generate_patent_drawings.py patent.json -o out --update-json
python <skill>/scripts/generate_patent_docx.py patent.json -o out/<名称>_专利申请文件.docx --require-drawings
python <skill>/scripts/export_patent_pdf.py out/<名称>_专利申请文件.docx -o out/<名称>_专利申请文件.pdf --preview-dir out/preview
ERROR from the checker and judge every WARN; rerun until clean. The checker catches the formal defects (single period, uncertain words, dependency rules, antecedent basis, 300-character abstract, figure numbering, reference numerals) that are easy to miss when re-reading your own text, and warns about scope problems (code field names or negative limitations in claims, an over-long or enumeration-heavy claim 1, a revision loop that no drawing shows, drafting remarks in the description).<名称>_专利申请文件_撰写说明.docx: it opens with the handover checklist (every 【待补充】, notes.handover, plus ownership, disclosure date and prior-art search), followed by disclosure risk, claim plan, support map, drawing sources and check results. Keep all non-application content there, not in the application file.Scripts need Python 3.9+. Pillow and a Chinese font are needed for drawings (the script refuses to render boxes-for-characters); LibreOffice for the PDF. If something is missing, install it or report the limitation — do not hand over drawings that show empty boxes.
Give the user the files and a short message: the invention points claim 1 rests on, the claim structure (e.g. "方法1–6 + 装置7 + 设备8 + 介质9"), where the handover checklist is (first section of the 撰写说明) and its most important items, the disclosure/novelty assessment, and a reminder that a patent attorney should review the draft and run a prior-art search before filing. Do not paste the checklist or your analysis.
notes, and requests for material into notes.handover.| Request | What to do |
|---|---|
| 把这篇论文写成专利 / 生成专利申请书 Word、PDF | Full workflow, direct mode |
| 先帮我梳理发明点再写 | human-in-loop: report disclosure status, invention points and claim plan; draft after confirmation |
| 只写权利要求书 | Steps 1–3, claims-drafting.md, run the checker on a JSON containing only claims |
| 根据说明书画专利附图 | drawing-generation.md; nodes and edges only from the text and paper figures |
| 检查这份专利草稿 / 是否忠实原文 | review: run the checker on the draft, then audit each claim feature against the paper using quality-checklist.md |
| 论文已经发表了还能申请吗 | Answer from patentability-and-disclosure.md; recommend confirming with a patent attorney |
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概要と使いどころ
<What it does, one line>. Trigger phrases: <phrases>.
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 wants to create or improve a presentation for an academic context — conference papers, seminar talks, thesis defenses, grant briefings, lab meetings, invited lectures, or any presentation where the audience will evaluate reasoning and evidence. Triggers include: 'conference talk', 'seminar slides', 'thesis defense', 'research presentation', 'academic deck', 'academic presentation'. Also triggers when the user asks to 'make slides' in combination with academic content (e.g., 'make slides for my paper on X', 'create a presentation for my dissertation defense', 'build a deck for my grant proposal'). This skill governs CONTENT and STRUCTURE decisions. For the technical work of creating or editing the .pptx file itself, also read the pptx SKILL.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。
Shortest English alias for web-adapt / universal-web-adaptation. Use this when adapting an unfamiliar public website generically before creating any site-specific rules.
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 to audit CLI adapter projects (like opencli) for missing output fields, then batch-generate fixes and submit PRs. Turns AI agents into adapter quality reviewers.
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。
Operate professional paid advertising across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for account intake, source-grounded audits, strategy, budget and measurement planning, creative production, experiments, reporting, monitoring, and explicitly approved campaign changes. Also trigger on PPC, paid social, retail media, attribution, tracking, landing pages, cross-platform conversion totals, negative keywords or search terms, beta-feature scoring, stale platform claims, API-token or credential setup, campaign deletion, and safe Claude Ads installation or uninstall.
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 Amazon Ads profiles, regions, Sponsored Products, Sponsored Brands, Sponsored Display, DSP, portfolios, targeting, search terms, retail readiness, creative, budgets, ACOS, TACOS, reporting, and policy. Use for Amazon Ads, sponsored ads, Amazon PPC, ACOS, TACOS, ASIN advertising, Amazon DSP, or retail-media optimization.
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。