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Found 22 Skills
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.
Use when deduplicating bounded collections.
Normalize messy creator campaign metrics from multiple sources into a single clean table with standardized field names ready to merge into your master tracker. This skill should be used when cleaning up influencer metrics, standardizing campaign data from multiple platforms, normalizing creator performance numbers, merging metrics from Instagram and TikTok and YouTube into one sheet, formatting messy analytics exports, preparing campaign data for a master spreadsheet, converting raw platform stats into a consistent format, combining metrics from different reporting tools, deduplicating creator data from multiple sources, fixing inconsistent column names across exports, or cleaning up a metrics dump before reporting. For calculating engagement rates, see engagement-rate-calculator-benchmarker. For full campaign reports, see campaign-roi-calculator. For parsing a single Story screenshot, see story-metrics-screenshot-parser.
Use when asked to parse, normalize, standardize, or convert dates from various formats to consistent ISO 8601 or custom formats.
Expert in high-performance CSV processing, parsing, and data cleaning using Python, DuckDB, and command-line tools. Use when working with CSV files, cleaning data, transforming datasets, or processing large tabular data files.
Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.
Build reliable data pipelines and analytics-ready datasets. USE when cleaning data, designing ETL/ELT, defining contracts, or shipping reproducible data workflows.
Handle messy CSVs with encoding detection, delimiter inference, and malformed row recovery.
Prepares and audits high-quality datasets for AI/RAG applications. Cleans noise, structure data, and ensures privacy compliance in knowledge bases.
WPS Spreadsheet Intelligent Assistant: Control Excel via natural language to solve pain points such as formula writing, data cleaning, chart creation, etc.
Activated when users need to extract decision-making signals from a large amount of unorganized information (user feedback, competitor data, market reports, internal metrics). Typical trigger signals: "Too much data, don't know which to trust" "Users are saying all kinds of things" "Received a lot of feedback but don't know what the conclusion is". Scenarios where it is not called: Information is already structured and clear, requiring only simple judgment; or there is no information at all and decisions need to be made based on intuition. Difference from the "Investigation Research Method": This skill focuses on information processing (how to process after obtaining raw materials), while the latter focuses on information collection (how to obtain good raw materials).
Validate and audit CSV data for quality, consistency, and completeness. Use when you need to check CSV files for data issues, missing values, or format inconsistencies.