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Found 56 Skills
Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk. Use before any workflow handles venue auth, user portfolio data, API keys, or trade planning.
When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.
Complete 9-step Clay enrichment workflow for 90%+ data coverage plus 58 Clay templates across 8 categories. Use when building enrichment workflows, setting up Clay tables, or maximizing data quality.
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Vendor-neutral skill to check a KPI dictionary for conflicting definitions, grain mismatches, and missing ownership.
Use when implementing data governance frameworks, building data catalogs, establishing data lineage, defining data quality rules, or setting up data stewardship programs - covers metadata management, data quality, and complianceUse when ", " mentioned.
Data pipeline and ETL automation - extract, transform, load workflows for data integration and analytics
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Comprehensive data validation using Pydantic v2 with data quality monitoring and schema alignment for PlanetScale PostgreSQL. Use when implementing API validation, database schema alignment, or data quality assurance. Triggers: 'validation', 'Pydantic', 'schema', 'data quality'.
Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.
Analyze messy and unstructured Excel files to identify data quality issues, detect format inconsistencies, find missing values, and generate comprehensive analysis reports. Use when Claude needs to work with Excel files (.xlsx, .xls) for data quality assessment, structure analysis, or when users request data auditing, cleaning recommendations, or statistical summaries of spreadsheet data.