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Found 21 Skills
Transform, filter, reshape, join, and manipulate football data. Use when the user needs to clean data, merge datasets, convert between formats, handle missing values, work with large datasets, or do any data manipulation task on football data.
Guides cleaning and standardizing tabular datasets before analysis, modeling, or reporting—profiling, quality rules, missing values, duplicates, outliers, type coercion, encoding fixes, record linkage, deduplication, high-level PII handling (not legal advice), actuarial/insurance field scrubbing, reproducible scrub pipelines, validation checks, and sign-off. Distinct from warehouse ETL or statistical modeling. Use when the user asks for "data scrubbing", "clean this dataset", "scrub the data", "data cleaning", "dedupe records", "handle missing values", "outlier treatment", "standardize columns", "data quality rules", "profile this table", or "prepare data for modeling". Not warehouse pipelines (data-warehouse-engineer), ML modeling (data-scientist, actuary), privacy programs (compliance-engineer), FinOps only (finops-analyst), or assumption governance (assumption-setting).
Prepares and audits high-quality datasets for AI/RAG applications. Cleans noise, structure data, and ensures privacy compliance in knowledge bases.
Use this skill when spreadsheet files are the primary input or output. This means the user wants to: open, read, edit, or repair existing .xlsx, .xlsm, .csv, or .tsv files (e.g., add columns, calculate formulas, format, create charts, clean messy data); create new spreadsheets from scratch or from other data sources; or convert between spreadsheet file formats. Trigger this especially when the user references a spreadsheet file by name or path—even casually (such as "the xlsx in my downloads")—and wants to process it or generate content from it. It's also used to clean or reorganize messy tabular data files (rows with incorrect formatting, misaligned headers, garbage data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do not trigger this 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 asked to parse, normalize, standardize, or convert dates from various formats to consistent ISO 8601 or custom formats.
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.
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
Coaches users to transform messy data into clean, analysis-ready formats using Power Query UI. Diagnoses data problems, visualizes goals, and guides step-by-step transformations.
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.