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Found 70 Skills
Guidance for counting tokens in datasets, particularly from HuggingFace or similar sources. This skill should be used when tasks involve counting tokens in datasets, understanding dataset schemas, filtering by categories/domains, or working with tokenizers. It helps avoid common pitfalls like incomplete field identification and ambiguous terminology interpretation.
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
Data analysis, visualization, and storytelling skill for financial and RevOps contexts. Use when: analyzing revenue data, building forecasts, cohort analysis, churn modeling, pipeline analytics, creating data-driven reports, building dashboards, cleaning messy data, sanity-checking analytical claims, exporting to Excel with formulas, or extracting data from PDFs. Features decision logging, bias-aware interpretation, and progressive disclosure (slide deck -> detailed report -> full notebook with all decisions documented).
Implement masked text input controls in WinForms applications. Use this skill whenever the user needs to create input fields with format masks (phone numbers, IP addresses, dates, currency), validate formatted input, restrict data entry to specific patterns, or configure how user input behaves with mask constraints.
Implement Syncfusion SfNumericTextBox for numeric input with formatting, validation, and customization in Windows Forms. Use when creating numeric input controls with currency formatting, percent values, number validation, or decimal formatting. Covers numeric formatting options, value range validation, and formatted numeric data entry with validation capabilities.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
Standards and best practices for writing LookML tests to ensure data integrity, accuracy, and logic validation.
Use when CSV, TSV, or Excel (.xlsx) is the primary input/output: inspect, transform, validate, convert, recalc formulas, or create/fix spreadsheets. Do not trigger when tabular data is incidental.
Clean and transform messy data in Stata with reproducible workflows
pytest, data validation, Great Expectations, and quality assurance for data systems
Data processing expert including parsing, transformation, and validation
Validate, format, and convert between JSON, YAML, and TOML. Parse and query structured data files. No API key required.