'Excel' 검색 결과

Compile a macroeconomic briefing for the UK, US, euro area, or Australia using official data. Summarize key indicators with green, amber, or red assessments, source citations, and publication dates, with options for selected topics and output formats.

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• Prepare economic background for a meeting
• Monitor inflation or labour market developments
• Compare economic indicators across countries

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.

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• Build a reproducible R or Quarto analysis workflow
• Compare regression models with progressively added controls
• Test robustness across samples and statistical methods

Review code changes for bugs, security risks, design issues, and test quality, then turn findings into specific, prioritized feedback. Use its checklists and comment templates to establish consistent review standards and support developer mentoring.

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• Reviewing pull requests
• Creating team review checklists
• Mentoring junior developers through feedback

Compare text and metadata extracted by two Tika builds from the same file collection. Generate reports to investigate content differences, encoding changes, new exceptions, and missing embedded documents, then repeat the evaluation after fixes.

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• Comparing extracted content before and after a Tika change
• Checking whether parser changes lose attachments
• Investigating new exceptions and large content differences

Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.

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• Summarizing and joining data files
• Exploring trends and distributions
• Cleaning data before analysis

Analyze tabular datasets too large to fit in memory using Vaex. Filter rows, calculate statistics, visualize distributions, convert file formats, and prepare machine learning workflows with operations that defer computation until results are needed.

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• Aggregate CSV files too large to fit in memory
• Explore large datasets with histograms and heatmaps
• Convert CSV data to HDF5 or Arrow

Diagnose file write failures by checking permissions, disk space, paths, and file locks. Get corrective steps before retrying, or check conditions before important writes to catch problems such as missing folders and read-only files.

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• Investigating permission errors when saving files
• Checking disk space before saving large files
• Resolving Windows path length problems

Implement filtering, aggregation, joins, and file processing with Polars for tabular data that fits in memory. Use lazy evaluation, which optimizes operations before running them, and parallel execution to improve pipelines and migrate pandas code.

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• Migrate slow pandas processing to Polars
• Filter and aggregate CSV or Parquet data
• Join multiple tables and reshape results

[omh] PPT, PDF, Excel, or HWP output to produce: decks, PDFs, spreadsheets, documents, HWP, Markdown, and binary export handoffs. Use when the user says: materials-package, material package, materials package, document package, deck file, binary export, file export, render qa.

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Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale. Triggers: "document translation", "batch translation", "translate documents", "DocumentTranslationClient".

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Modern frontend patterns for React Server Components, performance optimization, and Core Web Vitals

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CSV/spreadsheet formula injection (DDE, Excel/LibreOffice, Google Sheets IMPORT*). Use when exports, imports, or user fields feed spreadsheets or reporting tools.

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Review designs, products, and features with Steve Jobs' standards: ruthless simplicity, focus, and end-to-end excellence. Use when the user mentions "Steve Jobs review", "design review", "product review", "what would Steve do", "insanely great", "this feels too complicated", "too many features", "product taste", "saying no", or "is this good enough to ship". Also trigger when critiquing a UI, feature, or roadmap for focus and simplicity, cutting scope to the essential, or pressure-testing the whole experience from first run to daily use. Covers the simplicity audit, the no list, design-is-how-it-works, end-to-end ownership, demo culture, and a Jobs-style review protocol with binary verdicts. For visual design fundamentals, see refactoring-ui. For usability audits, see ux-heuristics. For detail polish, see microinteractions.

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Create, inspect, edit, recalculate, validate, render, and deliver Excel workbooks while preserving formulas, formatting, drawings, links, macros, and metadata.

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Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.

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读取、创建、修改和验证 XLSX，支持模板保留、公式重算和错误检查。

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SkillsBench task authoring — walk a contributor from idea to submission-ready task following CONTRIBUTING.md and the task-implementation rubric. Use when the user wants to create a new SkillsBench task, scaffold a task from an existing workflow (notebook, Excel workbook, document, dataset), convert a prompt or a benchmark item into a SkillsBench task, write skills for a task, or prepare a SkillsBench PR. Pairs with `task-review` (run that as a self-check before submitting).

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Read Microsoft Excel (.xlsx) files robustly with `openpyxl` (or `pandas`). Covers multi-sheet workbooks, header rows, empty cells, merged cells, comma-separated list cells, and converting a sheet to a list-of-dicts the rest of your code can consume. Use when a task input or reference document is an `.xlsx` file rather than JSON/CSV.

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Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).

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Use this skill when the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx base.

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Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.

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Für Ersatz: Sonderfall und Edge-Case-Prüfung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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小红书个人笔记数据复盘——基于用户自己创作者后台（创作中心/蒲公英）的笔记数据，做漏斗定位、卡点归因和多篇横向规律发现。当用户说"笔记数据复盘""这条为什么没流量""帮我看看这条数据""小红书数据分析""曝光高但没人点""涨粉不行""哪类内容该加码""我导出了后台数据帮我看看"，或直接贴出后台数据截图/CSV/Excel/几个数字时使用。只分析用户自己账号的笔记表现，不抓取平台数据、不做刷量。

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