Total 53,207 skills, Data Processing has 2692 skills
Showing 12 of 2692 skills
Collect posts from Reddit — text, images, likes, comments, timestamps. Use when the user wants to research content, track publishing activity, or export post datasets.
Collect following lists from TikTok — usernames the account follows, profiles, bios. Use when the user wants to map who an account follows or export following lists.
Collect trending notes from Xiaohongshu (RedNote) — trending notes, engagement, top creators. Use when the user wants to spot Xiaohongshu trends and viral notes.
Collect profile pages from TikTok — username, bio, followers, posts count, website. Use when the user wants to research creators, find influencers, or build lead lists.
Collect search results from Twitter/X — matching posts, URLs, authors, engagement. Use when the user wants to collect content matching keywords for research or monitoring.
Collect search results from Allegro — product titles, prices, ratings, URLs, sponsored flags. Use when the user wants to collect listings matching a keyword or category.
Collect boards from Pinterest — board title, pins, category, curator. Use when the user wants to research board curation or content collections.
Collect posts from Threads — text, images, likes, comments, timestamps. Use when the user wants to research content, track publishing activity, or export post datasets.
Collect following lists from Twitter/X — usernames the account follows, profiles, bios. Use when the user wants to map who an account follows or export following lists.
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.
Recommend appropriate chart types for experimental data with rationale and tool hints. Geography-aware: choropleth, spatial scatter, kernel density when spatial data detected. 为实验数据推荐合适的图表类型，支持地理空间数据可视化建议。
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, .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.