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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.
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All-in-one ComPDF workflow for document conversion, OCR, data extraction, PDF editing, protection, compression, and watermarking. Use for any ComPDF Server API request that processes PDF, Office, HTML, CSV, RTF, TXT, or image files.
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Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.
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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。
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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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Use this skill when a file has been uploaded but its content is NOT in your context — only its path at /mnt/user-data/uploads/ is listed in an uploaded_files block. This skill is a router: it tells you which tool to use for each file type (pdf, docx, xlsx, csv, json, images, archives, ebooks) so you read the right amount the right way instead of blindly running cat on a binary. Triggers: any mention of /mnt/user-data/uploads/, an uploaded_files section, a file_path tag, or a user asking about an uploaded file you have not yet read. Do NOT use this skill if the file content is already visible in your context inside a documents block — you already have it.
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Track Serenity / @aleabitoreddit on X and turn the feed into (1) a beginner-friendly Chinese iMessage digest with first-principles + Buffett-style judgement, (2) cumulative 60-day ticker mention analytics CSVs for a website, (3) a Xiaohongshu writing brief, and (4) a durable private research map. Trigger on requests like "follow aleabitoreddit / AleaBito / Serenity", "daily Chinese updates from that X account", "60-day mention analytics", "ticker mention count", "写小红书 aleabito", "aleabito 研究地图 / research map", or any request for Chinese commentary derived from @aleabitoreddit posts.
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Takes a curriculum framework and a statutory or accreditation requirement list; produces a coverage table, gap summary, and CSV showing which framework content covers each requirement and where gaps exist.
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Lightweight, script-driven variant of data2motion for turning data into a smooth, on-brand animated chart (one self-contained HTML) — built for a TEXT-ONLY model that cannot see its own output. You never write HTML/CSS/SVG/animation; you extract the data, pick a chart, fill a small JSON spec, and run one build script that owns 100% of the look and the motion, so the result cannot drift from the house template style or lose its smoothness. Use to turn a number/stat/table/CSV/paragraph into a quick animated chart, data reveal, or KPI when you want guaranteed template fidelity with minimal reasoning and no image feedback.
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Create, edit, analyze spreadsheets (.xlsx, .csv, .tsv). Use for Excel formulas, data analysis, visualization, formatting, pivot tables, charts, formula recalculation.
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Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.
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Fetch and export public X (Twitter) data with a self-contained Python CLI (twikit; no API keys, uses the user's own login cookies): profiles, a user's tweets/replies/likes, threads, search, followers, timelines, JSON/JSONL/CSV/SQLite output, resumable pagination. Use to scrape, archive, monitor, or analyze tweets or accounts.
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Generate and parse office documents — PDF, DOCX, XLSX, PPTX, CSV. Use when creating reports, exporting tabular data, or processing uploaded office files. NOT for project documentation (use docs).
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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".
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한국 스타트업·1인 법인 대표·프리랜서·개인사업자를 위한 장부 자동 생성 진입점 스킬. 호출 시 5개 하위 스킬(jangbu-import·jangbu-tag·jangbu-tax·jangbu-dash·jangbu-jongso)을 번호·문자 메뉴로 제시하고, 입력 즉시 해당 스킬 인터뷰로 직행한다. 엑셀·5대 은행 CSV·7대 카드사 명세서 PDF·영수증·세금계산서 지원, macOS Vision/PaddleOCR 로컬 처리, Level 2 민감정보 마스킹.
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세무용 재무제표(재무상태표·손익계산서) 자동 생성 스킬. 국세청 표준계정과목 매핑, 일반기업회계기준(K-GAAP) 간소화 버전. 분류 완료된 거래내역을 기간 지정해 BS·PL로 집계하고 JSON·CSV 파일로 출력.
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Personal finance assistant for budgeting, savings goals, investment tracking, debt optimization, tax preparation, insurance review, net worth tracking, and financial scenario modeling. Supports multi-currency, bank statement import (CSV/MT940/OFX), and locale-based tax rules. Use for any personal finance question: budget planning, expense tracking, portfolio allocation, FIRE calculations, debt payoff strategies, mortgage comparisons, tax deductions, insurance coverage, retirement planning, and life events with financial impact such as marriage, buying a house, changing jobs, having a baby, or going freelance.
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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".
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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".
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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.
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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.
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Turns a raw Qualtrics conjoint export into an analysis-ready long-format dataset, covering export settings, identifying the implementation, package or manual wide-to-long reshaping, choice-profile alignment, ratings, reference categories, pilot exclusion, and subgroup merges. Use when the user has a conjoint CSV to clean, reshape, or debug, cannot align choices with profiles, or asks what the data must look like before AMCE estimation. Design goes to conjoint-design, validity review to conjoint-diagnostics.
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Read or convert documents (PDF, Office, OpenDocument, RTF, EPUB, CSV) in a research workflow: read directly or convert, pick the converter from document structure, and decide whether the Markdown is tracked or scratch. Use when a document must be read, quoted, summarized, searched, extracted, or added to a source library; scans go to $vlm-ocr.
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Turns a raw Qualtrics conjoint export into analysis-ready long-format data: implementation identification, reshaping, choice-profile alignment, reference categories, and pilot exclusion. Use when a conjoint CSV needs cleaning, reshaping, or debugging before AMCE estimation.
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