「Excel」の検索結果

Cluster a list of keywords into topical groups with search intent labels, validated search volume, KD, and CPC data. Use this skill whenever a user provides a list of keywords (pasted, uploaded as CSV/Excel, or via Google Sheet URL) and asks to cluster, group, map, organize, or categorize them. Also trigger when a user says "keyword cluster", "cluster my keywords", "group these keywords", "keyword map", "keyword strategy from this list", "organize keywords by topic", or pastes or uploads a list of keywords and wants structure from it. Always use this skill — do not attempt keyword clustering manually without following this workflow.

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 image files into Word, Excel, PPT, PDF, HTML, RTF, CSV, TXT, or JSON with ComPDF. Use for screenshots, scans, receipts, forms, and photo-based business documents that need editable or structured 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。

Convert Word, Excel, PPT, HTML, TXT, CSV, RTF, PNG, and JPG files into PDF with ComPDF. Use when a business document or image needs fixed-layout PDF output for sharing, approvals, printing, or archiving.

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 PDFs to Word, Excel, PPT, HTML, RTF, images, CSV, TXT, JSON, Markdown, OFD, or editable PDF, and convert supported documents and images to PDF with ComPDF. Use for general bidirectional PDF conversion workflows.

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。

6-pillar architecture review framework. Adapted from AWS Well-Architected for use by great_cto's architect agent on every non-nano ARCH document. Forces explicit answers across operational excellence, security, reliability, performance, cost, and sustainability — not just feature design.

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。

飞书云空间：管理云空间中的文件和文件夹。上传和下载文件、创建文件夹、复制/移动/删除文件、查看文件元数据、管理文档评论、管理文档权限、订阅用户评论变更事件、修改文件标题（docx、sheet、bitable、file、folder、wiki）；也负责把本地 Word/Markdown/Excel/CSV 以及 Base 快照（.base）导入为飞书在线云文档（docx、sheet、bitable）。当用户需要上传或下载文件、整理云空间目录、查看文件详情、管理评论、管理文档权限、修改文件标题、订阅用户评论变更事件，或要把本地文件导入成新版文档、电子表格、多维表格/Base 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。

Builds Excel charts, pivots, validation, exports, Office Scripts/Graph automation, and workbook audits. Use when automating workbooks; for plain edits prefer official xlsx 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。

Converting document formats (Markdown/Word/Excel/PDF/HTML) and generating reusable conversion scripts. Use for distributable specs, accessibility-compliant PDFs, or Pandoc/LibreOffice pipelines.

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。

华安保险车险周报自动生成器（麦肯锡风格）。将周度车险数据（支持Excel/CSV/JSON/DuckDB）转化为12-13页董事会级经营分析报告。采用问题导向标题、16:9宽屏、四象限/气泡图可视化、深红#a02724配色。报告结构：经营概览、保费进度、变动成本、损失暴露、费用支出，每个章节分机构和分客户类别双维度分析。支持自定义阈值配置和保费计划。触发场景：用户上传车险周报数据文件（.xlsx/.csv/.json/.db），要求生成董事会汇报PPT。

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。

帮助保险代理人将pdf、Excel、Word、PPT等各类客户资料转化为个人客户信息资料库，方便小龙虾调用和使用，回答下列问题："帮我为下个月生日的客户撰写个性化的生日祝福"、"帮我为每个保单客户撰写一份个性化的理财活动邀约"，等等。

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。

Validate data quality in CSV/Excel files for vehicle insurance platform. Use when checking required fields, validating data formats, detecting quality issues, or generating quality reports. Mentions "validate", "check fields", "data quality", "missing values", or "quality score".

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。

Clean and standardize vehicle insurance CSV/Excel data. Use when handling missing values, fixing data formats, removing duplicates, or standardizing fields. Mentions "clean data", "handle nulls", "standardize", "duplicates", or "normalize".

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。

Analyzes vehicle insurance daily reports and signing lists. Use when user asks to analyze insurance data, generate business reports, check institution performance, monitor policy trends, or detect business anomalies. Handles Excel/CSV files with fields like premium, institution, customer type, and renewal status.

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。

Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

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。

Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall, OpenAlex), and (3) a browser-based fallback for paywalled publishers without TDM access (ACS, RSC, IEEE, AIP, IOP, APS, Annual Reviews, T&F, Chinese journals). Browser fallback offers two routes — Route A drives the user's logged-in Chrome via the OpenClaw `browser` tool with `profile="user"` (best for interactive sessions), Route B uses the standalone `auto-paper-harvester` CLI with its built-in Playwright (best for unattended bulk runs). Use when the user wants to harvest, scrape, fetch, or bulk-download papers from a DOI list, savedrecs export, or Excel; or wants to fill missing full-text PDFs for an existing literature collection. Triggers on phrases like "批量下载文献", "下载全文", "harvest papers", "scrape full text", "TDM API", "下载 Elsevier 全文", "Wiley 批量下载", "下载 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。

Reads or converts documents in a research workflow, including PDF, Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, and CSV. Chooses direct reading or conversion, selects a converter from document structure, and decides whether the Markdown is tracked or temporary. Use when a document must be read, quoted, summarized, searched, extracted, or added to a source library. Scanned documents go to vlm-ocr and bulk intake to research-repo.

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。

High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web 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。

不一书个人工作台生成器。通过一场引导式访谈，帮用户（包括完全不懂代码的人）做出一个属于自己的手机端个人工作台—— 左侧常驻功能栏、右侧卡片流，打开就是今日计划、打卡、记账、体重、日程、单词、树洞等自己每天真正要用的东西， 可以部署上线并添加到手机桌面，像一个 App 一样用。 当用户说"初始化不一书个人工作台生成器"、"初始化 bys-personal-dashboard"、"我想做一个自己的个人工作台"、 "抖音上那种个人工作台我也想要一个"、"把我的日常安排做成一个手机页面"时必须触发； 即使用户只是丢来一张别人的工作台截图说"我也想要这种"，也应触发。 已有工作台后说"给我的工作台加个XX功能"、"工作台换个配色"、"工作台改一下"时，走迭代流程而不是重新初始化。 用户没说"工作台"这个词时，用三个特征判断：①在手机上用 ②每天都会打开 ③装的是用户自己的私人数据 （体重、账目、打卡、待办、日记）。三个同时成立就该触发，比如"我想把体重记账待办整理到一个地方、 手机上能点开"——这就是工作台，只是用户不知道这个叫法。三个缺任何一个都不要触发。 功能只有一个也算——"我老忘记吃药，想每天在手机上勾一下"就是种子需求，别因为"听起来只是个小功能"就不触发。 判据是数据属于谁，不是数据存在哪：用户自己的记账 Excel 想做成手机上每天看的页面，是工作台；公司销售数据做报表，不是。 不用于：用完就走、不存个人数据的小工具页（抽奖转盘、计算器、放在活动大屏或直播间给别人看的倒计时）、 后台管理系统、 给团队用的多人协作工具、个人博客/作品集/对外展示的个人官网（那些是给别人看的，不是自己每天用的）、 公司或业务数据的报表看板（用 xlsx 之类的技能）、与个人工作台无关的普通 HTML 编码问题。

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。

Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.

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。

Expert support operations guidance for customer service excellence. Use when designing ticket management systems, creating SLA policies, building support tier structures (L1/L2/L3), optimizing knowledge bases, defining severity levels and escalation procedures, implementing support metrics (CSAT, FRT, TTR, FCR), configuring support tool stacks, or building support-to-CS feedback loops. Covers Zendesk, Intercom, Freshdesk, and help desk best practices.

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。

Excellence-first strategic decision support. This skill should be used when users need to choose between options, pressure-test a plan, evaluate risk, or make a specific decision — across security, product, growth, operations, org design, and finance. Use when there is a decision with tradeoffs to score and a recommendation to make. For pure research ("what does the best in the world do about X?"), use best-in-world-research instead.

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。

Reviews startup financial models for investor readiness: validates unit economics, stress-tests runway scenarios, and benchmarks metrics against stage-appropriate targets. Use the full review, about 10–15 minutes, when the user provides a financial model (Excel, CSV, or a model pasted as text) and asks for review, validation, runway analysis, or unit-economics scoring. For a single money question with figures to compute, such as 'how long do I have?' or 'is a 4x burn multiple bad?', use its shorter quick check, so the answer comes from the calculator and dated benchmarks, not mental arithmetic.

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 all submittal requirements from specification sections and generate a comprehensive submittal register in Excel. Triggers: 'submittal log', 'extract submittals', 'submittal register'. Requires /spec-splitter 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。

Count-based quantity takeoff and tag completeness auditing for construction drawings. Vision + OCR reconciliation, sheet markup, Excel QTO output. Triggers: 'tag audit', 'quantity takeoff', 'QTO', 'count fixtures'.

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。