Browse Skills

Automates accessibility audits for web applications, identifying issues like missing alt text, contrast problems, and ARIA violations. Use it to ensure your frontend meets WCAG standards.

A template for creating Claude Code agent skills, helping developers quickly scaffold and customize new skills for AI-assisted coding.

A meta-skill that helps you install, manage, and discover Claude Code skills from a public directory. Useful for extending Claude's capabilities with community-built plugins.

Get structured design feedback on usability, visual hierarchy, consistency, and accessibility. Use it when reviewing Figma designs, mockups, or screenshots to identify improvements at any stage.

Audits a design or page for WCAG 2.1 AA compliance, checking color contrast, keyboard navigation, touch targets, and screen reader behavior before handoff.

Translates natural language data requests into optimized, dialect-specific SQL queries with best practices. Ideal for building complex queries, optimizing performance on large tables, and getting syntax for Snowflake, BigQuery, Postgres, etc.

QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

Profiles and explores a dataset to understand its structure, quality, and patterns. Use when encountering a new table or file to check null rates, column distributions, spot quality issues, and decide which dimensions and metrics to analyze.

Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

Extracts tribal knowledge from data analysts about their company's data warehouse, terminology, metrics, and query patterns, then generates a tailored data analysis skill with reference files.