'csv' 검색 결과

Create, edit, and inspect spreadsheets for reusable budgets, models, and trackers. Check formulas and dependent totals, preserve unrelated data, and deliver an editable workbook or a link to the updated native spreadsheet.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a monthly headcount budget
• Investigate an incorrect quarterly total
• Fix formulas and highlight changed cells

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Simulate concentration changes over time from biochemical models in SBML or Antimony, and compare fixed parameter conditions. Checks model units and exports CSV results plus COMBINE archives containing models and experiment settings, then replays them to compare results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate concentration changes in reaction networks
• Compare baseline and altered kinetic parameters
• Inspect model units and consistency warnings

Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording agent evaluation results in a shared format
• Comparing a candidate agent with a baseline
• Tracking actual cost per solved task

Turn broker trade exports into a report on holding periods, trading frequency, win rate, and realized profit or loss. Diagnose patterns such as holding losers longer, overtrading, chasing rising prices, and fixation on particular price levels, with numerical evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing win rate, profit or loss, and holding periods
• Checking trading habits with numerical evidence
• Comparing results by month, symbol, or market

Evaluate investment factors across multiple securities using statistical measures and grouped backtests. Compare how well each factor predicts future returns, examine its consistency over time, and combine useful factors with equal or performance-based weights.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing whether an investment factor predicts returns
• Comparing factor strength across industries and markets
• Checking how predictive power changes with holding periods

Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating backtest execution errors
• Finding why a strategy produces no trades
• Checking delayed trades or low capital usage

Describe your ideal customer in plain language to find matching companies and decision makers. The skill adds company details and contact information, then presents a ranked lead table showing how closely each prospect matches your criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find sales prospects by industry and company size
• Identify decision makers with specific job titles
• Prioritize leads by fit with your ideal customer

Use sales history and current stock to estimate stockout dates, reorder timing, quantities, and costs. Identify cash tied up in slow-moving inventory, and prepare purchase order and supplier email drafts for approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding what to reorder this month
• Checking whether stock will last until delivery
• Finding cash tied up in slow-moving stock

Coordinate month-end reconciliation, owner sign-off, and a 30/60/90-day cash forecast based on closed books. Combine the results into a reporting packet with financial explanations and open items, with approval required before external delivery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reconcile monthly books against payment data
• Explain changes in revenue and margin
• Refresh cash forecasts using closed-month figures

Explore a table or data file before starting analysis. Summarize its structure, missing values, duplicates, and distributions, flag potential quality issues, and suggest useful measures, grouping columns, and follow-up analyses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understanding an unfamiliar table or data file
• Checking missing values, duplicates, and suspicious entries
• Choosing measures and grouping columns for analysis

Turn spreadsheets, query results, or pasted data into clear Python charts. Get help choosing chart types, formatting labels and colors, and producing static images or interactive charts with hover details and zoom.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Chart monthly revenue trends for a report
• Compare product scores in a ranked bar chart
• Explore data with hover details and zoom

Design agents that read, update, summarize, or sync spreadsheet data. Define how they select the right table, check existing values, verify writes, request confirmation for destructive changes, and report the exact rows and ranges processed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an agent that refreshes a leads sheet each morning
• Build weekly sales reports from spreadsheet data
• Define an agent that syncs data between two systems

Define product KPIs, design dashboards, and analyze retention, feature adoption, and conversion. Choose metrics suited to each product stage, compare groups of users over time, and turn meaningful changes into concrete product actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing KPIs for each product stage
• Designing dashboards for different audiences
• Comparing retention across signup groups

Convert office documents, spreadsheets, presentations, ebooks, and PDFs into Markdown text that an agent can read. Save large conversions to a file and use hosted text recognition for scanned PDF pages when needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare Word documents for an agent to read
• Convert spreadsheets and slides into Markdown
• Extract content from PDFs and ebooks

Compare machine learning experiment results from JSON or CSV files. Calculate improvements over a baseline, summarize variation across repeated runs, identify trends and unusual results, and suggest experiments to test possible explanations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare model performance across experimental conditions
• Check variation across runs with different random seeds
• Identify trends as a parameter changes

Turn an arXiv paper URL or PDF into extracted text, word frequency tables, and reference links. Create additional materials such as a two-person audio discussion, an interactive webpage, presentation slides, and a visual summary.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyze frequent terms and reference links in a paper
• Turn a paper into a two-person audio discussion
• Prepare presentation slides and a visual summary

Read, write, and transform workspace files by running Python through run_shell when execute_code_sandbox cannot access them. The workflow covers explicit file paths, batch processing, error handling, and checking generated outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reading configuration files in the workspace
• Transforming CSV or JSON data files
• Processing multiple text files in a batch

Analyze numerical data in Excel with box plots and an outlier summary. Extract error values from text containing the ± symbol, chart their distribution, and export statistical results as a CSV report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare numerical distributions across spreadsheet columns
• Count outliers using the interquartile range
• Extract error values from text and chart their distribution

Generate Python code to retrieve economic indicators and financial prices from sources including FRED, World Bank, BLS, OECD, IMF, and Yahoo Finance. Scripts include data cleaning, indicator documentation, and error handling for reusable datasets and updates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Downloading GDP, unemployment, and inflation data
• Building datasets across countries and sources
• Automating updates for ongoing research

Convert documents, images, audio, web pages, and structured data into Markdown for AI analysis. Preserve headings, lists, tables, and links, extract text through OCR or transcription, and process multiple files or ZIP archives together.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting PDF and Office documents to Markdown
• Extracting text from images and audio
• Extracting web content and YouTube transcripts

Use this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.