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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Place pre-screened literature citations into a LaTeX or Word manuscript, or restyle the citations already in one. Three modes: (1) inline placement — inline \cite{}/\citet{}/\citep{} with a compiled references.bib, for author-date journals (APA, MLA, Harvard, Chicago author-date, IEEE, Vancouver); (2) footnote placement — full formatted \footnote{} or OOXML footnotes for legal and notes styles (Bluebook, OSCOLA, Chicago, APA, McGill) with Id./supra short forms; (3) restyle — convert existing footnote citations from one style to another. This skill is manual-invoke ONLY — trigger ONLY when the user explicitly runs /cite-placement or explicitly names the "cite-placement" skill. Do NOT auto-trigger on general citation, footnote, or reference requests.

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Convert between LaTeX and Microsoft Word for academic manuscripts in either direction, and assemble .tex from mixed sources. Fires on: converting .tex/LaTeX to Word/.docx ("tex to docx", "latex to word", "tex2docx", "convert to word", "pandoc convert"); converting .docx manuscripts to LaTeX for editing and back ("convert to latex", "manuscript", "footnotes", "reference doc", the docx-to-tex-to-docx round-trip / academic paper editing pipeline); high-fidelity delivery where plain pandoc loses tables, mangles cross-references, or fails on custom macros — booktabs/regression tables, OMML equations, cleveref, \estauto, \@@input, \thanks, TikZ, native Word tables, longtable, siunitx; and building .tex from mixed PDF/docx/LLM-generated sources. Replaces and reroutes the retired skills manuscript-editing-template-latex, latex-to-docx-fidelity, tex2docx, and latex-from-mixed-sources.

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Convert Markdown (.md) files to a polished PDF with ALL images preserved and scaled to the page. Use whenever the user asks to "save this markdown as a PDF", "convert README.md to pdf", "export the .md as a pdf", "turn these notes/docs into a PDF", or wants a PDF deliverable of any GitHub-flavored Markdown document (README, design doc, report, notes) — especially when it contains images or diagrams (remote or local), tables, code blocks, or a linked table of contents. Pipeline: pandoc (GFM → standalone HTML with embedded resources) → headless Chrome print-to-pdf with a GitHub-like print stylesheet → pypdf verification that every referenced image is embedded. Do NOT use for .tex → PDF (use a LaTeX toolchain) or for .docx work (use word-docx / tex2docx).

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Product Discovery through an 18-task methodology in 6 blocks — from market analysis to an investor-ready financial plan. Use when someone wants to run Product Discovery, validate a startup, assess a market hypothesis, prepare for an investment round, or raises an adjacent topic — idea validation, niche sizing, competitor teardown, product-market fit, pitch preparation, startup unit economics, geographic expansion into a new country, go-to-market for SEA / Asia / MENA. People rarely name PD explicitly; they say 'is there a market for this', 'assess my idea', 'I need a deck for an angel', 'we are entering Thailand/Singapore/UAE, how do we size it'. Use the skill in those cases too. Three modes: Light (~45 min, idea stage), Full (~2-3 h, MVP with customers), Geographic Expansion (~2 h, product entering a new geography). Produces 3-4 artifacts in /mnt/user-data/outputs/: one-pager.pptx, financial-plan.xlsx, presentation.pptx, optionally interview-guide.docx.

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**CONVERSION SKILL** — Convert binary/Office documents to Markdown for LLM consumption. USE FOR: converting Word, Excel, PowerPoint, PDF, Visio, MS Project files to Markdown, batch document conversion, extracting text from non-readable formats. DO NOT USE FOR: Markdown formatting/linting, general file I/O, web scraping, image generation.

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**WORKFLOW SKILL** — Generate formatted documents from Markdown or structured content. USE FOR: creating PowerPoint presentations, Word documents, Excel spreadsheets, MS Project files, PDFs from Markdown/JSON/chat context using templates. DO NOT USE FOR: converting documents TO Markdown (use doc-to-markdown), general file I/O, web scraping.

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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".

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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。

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Use when 用户想根据论文、科研文档、研究文本、图注、开题/答辩材料或具备读图条件的参考图生成可复制的 AI 科研绘图提示词；触发场景包括论文配图、科研绘图、图形摘要、Graphical Abstract、机制图、技术路线图、实验装置图、模型架构图、多面板对比图、汇报总览图、参考图风格迁移、未知领域母版生成，或上传/引用 PDF、Word、LaTeX、Markdown、摘要、方法段、图注、课题说明、研究方案、参考图片。Also use when the user asks for scientific figure prompts, research illustration prompts, graphical abstract prompts, mechanism diagrams, technical roadmaps, experimental setup diagrams, model architecture diagrams, multi-panel scientific figures, paper/conference figures, or AI image prompts based on papers, research documents, abstracts, methods sections, figure captions, proposals, reference images, PDFs, Word docs, LaTeX files, Markdown files, or research plans.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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中国大陆地区法院类案快速检索与《案件检索报告》（DOCX）生成技能。以案件五项要点（案由/法律关系、争议焦点、关键事实、我方诉请与诉讼地位、请求权基础法条）为检索锚点，经一次四问确认即开工（其中**检索地域范围**与**裁判时间范围**为强制选择题，默认范围为「2021年1月1日以后裁判的浙江省范围内的法院案例」，用户点选默认后须回显该范围提示）；按**请求权基础、法律关系、案件事实三要素**判定高匹配——用户**指定**检索特定结果的案例时结果相似度计入评分（四要素满分 10 分），用户**未指定**时结果相似度不作为高匹配考虑因素（三要素满分 8 分）；r1 判定须**核对法条版本与时间效力**，援引已失效旧法条者不得给满分；以语义向量检索为主召回、精确结构检索为条件兜底，本地脚本加权打分后按「得分率 ＞ 法院层级 ＞ 审判程序 ＞ 裁判日期」排序，并检测**同院同日同标题的重复入库**案件、疑似重复者不占入报名额；截取匹配度最高的 5–10 例类案，逐案调详情核验案号并摘录判词原文，最后直接输出含页脚页码的 DOCX 报告（默认保存至桌面）。全流程人工确认 1 次、检索类调用控制在 2 次召回 ＋ ≤10 次详情以内，报告阶段检索冻结。未指定结果时三要素高匹配而结果相反者亦会入报（逐例标注结果方向），指定结果时结果相反者一律剔除；报告内以两处固定声明披露「未设不利先例定向检索路径」的局限。当用户要求「快速检索类似案例」「找几个正向案例」「快速版案例检索报告」「检索 5 到 10 个类案并出报告」「出一份类案检索报告 docx」时使用。需要四象限归位、反向案例定向检索、补充召回多轮迭代或 14 分制硬软条件精判时，应使用完整版技能「案例检索报告Plus」而非本技能。基于华宇元典法律数据 MCP。

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[user] Business Legal Counsel / CLO level contract drafting, review, and legal risk assessment. Handles domestic Chinese contracts (中文), cross-border contracts, and offshore international contracts in Chinese and English. Jurisdictions: PRC law (Civil Code, Company Law, Anti-Unfair Competition Law), US law (UCC, common law), Hong Kong law (common law, HK ordinances). Combines relevant laws, regulations, and litigation case precedents in contract analysis. Generates Word (.docx) files with track changes and persuasive margin comments for contract review markup. Use when user mentions contracts, legal review, contract drafting, legal risk assessment, NDA, agreement, 合同, 法律, 审核, 起草, 合同审查, 法务, legal negotiation, due diligence, 尽职调查, 风险评估, 条款谈判, 反不正当竞争, or any contract-related task.

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案件分析报告（参考王泽鉴请求权基础分析法）Plus。 作者：浙江金道律师事务所 龚家勇律师。 触发条件（用户说出以下任一内容时激活本技能）： - "分析案件" / "写案件分析报告" - "用请求权基础分析法" / "王泽鉴方法" - "案件研判" / "案件论证" - 用户上传案件说明或材料并要求进行法律分析 本技能将： 1. 引导用户提供案件材料（如尚未提供） 2. 内置本地知识库（《建工解释二》全文+典型案例+法答网第三十八批），优先于华宇元典MCP远程检索 3. 通过"华宇元典法律数据"MCP检索相关法律法规、司法解释、类似案例（2021年1月1日及之后裁判，按管辖法院所在省挑选，首选浙江省；建议选取3个案例，用户可自行决定数量）和最高人民法院《法答网精选答问》 4. 参考王泽鉴请求权基础分析法，生成结构化《案件分析报告（参考王泽鉴请求权基础分析法）Plus》（Markdown格式），报告结构为：案件事实摘要 → 参考结论与应对建议 → 法律法规依据 → 法律问题与争议焦点 → 请求权基础检查（六层） → 请求权竞合处理 → 参考案例 → 相关程序问题分析 → 法答网精选答问参考（可省略） → 其他需要注意的内容 5. 报告完成后主动询问用户是否需要转换为 Word（.docx）格式，转换后格式与内容与 Markdown 版本完全一致 6. 在报告末尾注明"本报告由AI生成，仅供参考" 前置提醒：本技能效果最佳需提前连接"华宇元典法律数据"MCP。 若未连接，仍可进行案件分析，但法律法规和案例检索需手动补充。

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法律关系分析法案件分析技能。当用户要求"分析案件""写案件分析报告""用法学方法分析""法律关系分析法" "案件研判""案件论证"或上传案件材料（起诉状、判决书、证据材料、合同等）并希望进行法律分析时使用。 内置本地知识库（《建工解释二》全文+答记者问+典型案例、法答网第三十八批、方法论详解），优先查阅； 法律法规与类似案例的补充检索使用华宇元典（yuandian-mcp），法答网精选答问检索调用「法答网精选答问Plus」技能； 按照法律关系分析法两阶段五步骤框架，生成结构化的《案件分析报告（法律关系分析法Plus）》（Markdown格式）； 报告完成后主动询问用户是否需要转换为 Word（.docx）格式，转换后格式与内容（含第八部分依法省略时的编号调整）与 Markdown 版本完全一致。

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民事案件卷宗归档助手 V3。用户提出归档案件、先归档现有材料、完卷、补齐材料、生成归档表格或合并卷宗 PDF 时使用。按律所配置分类材料、提取字段、可靠填写 Word 表格、生成缺失清单；合并 PDF 必须先取得用户确认。

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案件预算和持续的 WIP/偏差监控。在案件设立时构建分阶段费用估算，按法域或工作流运行自下而上预算，计算应急准备金，并构建 AFA 安排（固定费用、上限费用、分阶段固定费用）。持续监控：对照预算的 WIP 跟踪、比例性评估（支出与进度）、带根本原因分析的偏差评述、完成预测、变现率监控、核销分析。触发词：'build a budget'、'fee estimate'、'what will this cost'、'WIP review'、'budget vs actual'、'how are we tracking against budget'、'we're over budget'、'realisation is poor'、'what's our ETC'、'budget for the German workstream'、'model the financial impact of this scope change'、'draft a fee adjustment'、'write-off analysis'、'how much contingency'、'AFA structure'、'fixed fee estimate'、'budget update'、'forecast to complete'。

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凭 App Key 调用百炼®标书开放 API，完成「招标文件智能解读 → 抽取分包 → 生成成品投标文件(.docx) → 可选合规审查」的端到端标书制作。当用户提供招标文件并希望生成投标文件/标书、对已生成标书做合规检查、或询问百炼®标书相关能力时使用。本 skill 是百炼®标书线上接口的轻客户端，不复刻其算法。

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上传投标文件（可配套招标文件），AI 输出分级合规审查报告：高风险/待复核/提示三级清单，每条附招标依据与修改建议，支持多文件雷同检测。当用户想审查标书合规性、排查废标风险、做雷同检测时使用本 SKILL。需百炼®标书 App Key（新用户可零输入免费试用或手机号注册，均赠积分）；文件经百炼®标书云端处理。

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一站式法律服务平台 — 专业法律咨询 + 要素式/通用起诉状起草 + 刑事专项材料辅助 + 法条分析与法规检索。flk.npc.gov.cn API实时法条优先，不可用时AI知识库降级兜底；以22类法律领域覆盖矩阵约束路由边界；支持35个起诉状模板、5类刑事专项材料辅助；内置Logic Doctor逻辑自检、诉讼时效追踪及10类异常容错处理（含连续失败熔断）；渐进式意图收敛协议（3轮状态机）；混合意图自动任务拆解（按依赖排序→合并→顺序执行）。非标准案由自动使用通用模板。

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法国法领域的法律助理专家。法律研究、法律咨询、法律文书起草、反论证、合同与文件分析、法律动态跟踪、参考文献核验与统一。生成 Word 文档（computer use）或结构化的对话式答复。MCP：OpenLegi、Themia、LegalDataHunter。任何法律问题、任何法国法概念（合同、责任、判例、民法典、法院、损害赔偿）、任何关于欧盟法、欧洲法院（CJUE）或欧洲人权法院（CEDH）的问题，以及任何法国法律参考文献核验或统一的请求，均触发本技能。

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