Results for “csv”

Prioritize inbound HubSpot leads using engagement, company fit, urgency, and recent activity. Get a call list with talking points, follow-up email drafts, and proposed call times, with a CSV or pasted-list fallback.

• Choosing which leads to call first today
• Preparing talking points for each prospect
• Drafting follow-up emails

Run a saved weekly or monthly report and combine its results with a brief business snapshot. Deliver one briefing with an XLSX workbook, using a connected data source or uploaded files, and reuse the definition for scheduled runs.

• Receive the same weekly or monthly report on schedule
• Review report figures alongside cash and sales context
• Rerun a saved report from CSV or XLSX data

Turn CSV files, pasted data, or query results into an HTML dashboard that opens in a browser. Combine key metrics, interactive charts, filters, and sortable tables in one report, with sample data available when real data is missing.

• Create a dashboard of key metrics for executives
• Turn query results into a shareable report
• Review team metrics with filters

Analyze git history to map contributors to security-sensitive files, identify reliance on too few people, and find stale areas. Export CSV and JSON results for ownership reviews, graph exploration, and comparison with CODEOWNERS.

• Find stale security-sensitive code with few maintainers
• Check reliance on individual authentication or cryptography maintainers
• Compare CODEOWNERS with actual contribution history

Create and edit spreadsheets for budgets and editable tables, including targeted cell updates and added records. Save in ODS by default, or use XLSX when Excel compatibility or supported embedded charts are needed.

• Creating a budget spreadsheet
• Turning CSV or Markdown tables into spreadsheet cells
• Updating cells or adding records to an existing workbook

Create paper figures and LaTeX comparison tables from experiment data in JSON or CSV files. Save reproducible plotting scripts, use consistent styling, and check rendered figures for readability, fair comparisons, and captions supported by the data.

• Plotting training curves
• Comparing methods and ablation results
• Combining results across datasets into multiple panels

Create and edit 飞书 spreadsheets, summarize data, build charts, and verify formulas. Adjust formatting, import or export Excel and CSV files, and review version history for routine reporting and financial modeling.

• Summarize sales data with charts and pivot tables
• Adapt Excel formulas for 飞书 spreadsheets
• Standardize formatting across worksheets

Select random winners from lists, CSV files, Excel, or Google Sheets. Choose multiple winners without duplicates, exclude previous winners, apply entry-based weighting, and record the selection method and timestamp for review.

• Drawing winners for social media giveaways
• Selecting event raffle winners and alternates
• Randomly choosing survey or test participants

Analyze git history to map contributors to security-sensitive files, identify reliance on too few people, and find stale areas. Export CSV and JSON results for ownership reviews, graph exploration, and comparison with CODEOWNERS.

• Find stale security-sensitive code with few maintainers
• Check reliance on individual authentication or cryptography maintainers
• Compare CODEOWNERS with actual contribution history

Read and summarize local text files, including notes, configuration files, source code, and logs. Extract relevant passages, explain key fields or table columns, and review recent errors in large logs without displaying the entire file.

• Summarize local notes and Markdown documents
• Review key fields in JSON or YAML files
• Understand CSV or TSV column structure

Turn supplied data into a newspaper-style chart with a takeaway headline, annotations, and source notes. Create line, bar, or range-band charts, which show an interval around a central line, with staggered animation in a single HTML file.

• Create a chart segment for a video
• Prepare a data chart card for Twitter
• Visualize CSV or JSON data with a takeaway headline

Find input files in confirmed workspace folders, save outputs to explicit locations, and check the results against task requirements. Useful in restricted execution environments where familiar folders may be missing or inaccessible.

• Finding input files in an unfamiliar workspace
• Checking generated spreadsheet sheets and formulas
• Validating CSV or JSON outputs before delivery

Move Python execution to saved script files when execute_code_sandbox repeatedly fails or becomes unstable. The skill covers writing single or multiple files, running them through run_shell, inspecting outputs and errors, and removing temporary scripts afterward.

• Switch execution methods after repeated sandbox failures
• Run Python code split across multiple files
• Process JSON input and write CSV output

Continue file reading and data processing when execute_code_sandbox or read_file fails. This skill runs multiline Python through run_shell, with examples for text, JSON, spreadsheets, PDF and Word documents, plus basic summaries and grouped calculations.

• Reading text or JSON when read_file fails
• Inspecting Excel and CSV data through Python
• Extracting text from PDF and Word documents

Continue Python tasks when the e2b sandbox, an isolated execution environment, is unavailable. Guides switching from execute_code_sandbox to run_shell, preparing dependencies, checking execution results, and saving intermediate data between calls.

• Continue Python processing during an e2b service outage
• Run CSV summaries after sandbox connection failures
• Execute multiline Python scripts through run_shell

Check files created or edited by another agent, subprocess, or tool against explicit requirements. Verify the actual location, structure, counts, and key fields, then rerun with absolute paths when the results contradict the completion report.

• Checking files produced by another agent
• Verifying spreadsheet sheets and row counts
• Checking CSV headers and record counts

Create, edit, and analyze spreadsheets with formulas, formatting, and visualizations. Preserve existing workbook conventions, keep calculations responsive to input changes, and use formula recalculation and error checks to verify the resulting files.

• Create a spreadsheet with formulas and formatting
• Update an existing workbook while preserving formulas
• Analyze and visualize Excel or CSV data

Search for social media profiles associated with a person, brand, or business across platforms including Facebook, Twitter, Instagram, LinkedIn, and TikTok. Receive a downloadable CSV with profile links, follower counts, page titles, and description snippets.

• Research a brand's presence across social platforms
• Find competitors' social media profiles
• Locate creators across multiple platforms

Build interactive maps with AntV L7, a library for displaying geographic data. The skill guides data loading, points, routes, areas, heatmaps, map interactions, animations, and performance tuning for large datasets.

• Display locations from GeoJSON or CSV
• Animate routes on a map
• Show geographic density with a heatmap

Analyze git history to map contributors to security-sensitive files, identify reliance on too few people, and find stale areas. Export CSV and JSON results for ownership reviews, graph exploration, and comparison with CODEOWNERS.

• Find stale security-sensitive code with few maintainers
• Check reliance on individual authentication or cryptography maintainers
• Compare CODEOWNERS with actual contribution history

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.

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

Analyze bulk RNA-seq counts in Python to find genes whose expression differs between conditions. Prepare data, account for batch effects, adjust statistical results for multiple testing, and export tables with volcano or MA plots.

• Comparing gene expression between treatment and control
• Accounting for batch effects or age in expression analysis
• Moving R-based DESeq2 workflows to Python

Find potential customers based on your product and ideal customer profile, a description of companies you want to sell to. Research each prospect, score its fit with supporting evidence, and compile reports and a CSV.

• Find companies that match an ideal customer profile
• Research potential customers before outreach
• Prioritize prospects using evidence-based scores

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

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