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Find potential influencer partners across Instagram, Facebook, YouTube, and TikTok using Apify data extraction tools. Collect profile and engagement metrics, assess brand fit and authenticity signals, and present shortlisted candidates in chat or export data as CSV or JSON.
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Analyze audiences across Facebook, Instagram, YouTube, and TikTok using Apify Actors, tools that collect platform data. Gather follower, profile, or comment data, summarize audience characteristics and engagement patterns, and return results in chat, CSV, or JSON.
中文简介正在准备中。当前显示英语简介。
Convert local images, audio, video, documents, and data using tools installed on the computer. Preserves original files, keeps conversion local, and checks outputs for content, format, and relevant quality changes before reporting the result.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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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Read, transform, analyze, and generate Excel and CSV files. Use when a user asks to open a spreadsheet, process Excel data, merge CSVs, create pivot tables, clean up data, convert between Excel and CSV, add formulas, filter rows, or generate reports from tabular data. Handles .xlsx, .xls, and .csv.
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Turn agent activity and transcript token counts into a cost report organized around completed outcomes. Separate estimates from measured spending, examine waste and rework, and export readable HTML with supporting JSON, CSV, and evidence.
中文简介正在准备中。当前显示英语简介。