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Found 95 Skills
Validates JSON data against JSON Schema using the z-schema library. Use when the user needs to validate JSON, check data against a schema, handle validation errors, use custom format validators, work with JSON Schema drafts 04 through 2020-12, set up z-schema in a project, compile schemas with cross-references, resolve remote $ref, configure validation options, or inspect error details. Covers sync/async modes, safe error handling, schema pre-compilation, remote references, TypeScript types, and browser/UMD usage.
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.
Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation in FastAPI, Django, and configuration management.
Performs technical SEO audits covering site speed, crawlability, indexability, mobile-friendliness, security, and structured data. Identifies technical issues preventing optimal search performance.
Expert guide for Schema.org structured data and JSON-LD implementation. Use when creating schema markup, validating structured data, implementing rich results (FAQ, HowTo, Product, Article, LocalBusiness, Breadcrumb, Organization, etc.), troubleshooting rich snippet eligibility, or understanding Google's structured data requirements.
OmniStudio Data Mapper (formerly DataRaptor) creation and validation with 100-point scoring. Use when building Extract, Transform, Load, or Turbo Extract Data Mappers, mapping Salesforce object fields, or reviewing existing Data Mapper configurations. TRIGGER when: user creates Data Mappers, configures field mappings, works with OmniDataTransform metadata, or asks about DataRaptor/Data Mapper patterns. DO NOT TRIGGER when: building Integration Procedures (use sf-industry-commoncore-integration-procedure), authoring OmniScripts (use sf-industry-commoncore-omniscript), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.
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.