Data cleaning, preprocessing, and quality assurance techniques
Detects missing values, outliers, and duplicates in datasets, then applies appropriate cleaning techniques like imputation, normalization, and validation to ensure data quality for analysis.
/plugin marketplace add pluginagentmarketplace/custom-plugin-data-analyst/plugin install data-analyst-roadmap@pluginagentmarketplace-data-analystThis skill inherits all available tools. When active, it can use any tool Claude has access to.
assets/config.yamlassets/schema.jsonreferences/GUIDE.mdreferences/PATTERNS.mdscripts/validate.pyMaster data cleaning and preprocessing techniques essential for reliable analytics.
| Error Type | Cause | Recovery |
|---|---|---|
| Memory error | Dataset too large | Use chunking or sampling |
| Type conversion failed | Invalid data format | Apply preprocessing first |
| Encoding issues | Wrong character encoding | Detect and specify encoding |
| Validation failure | Data doesn't meet schema | Review and adjust validation rules |
Use when working with Payload CMS projects (payload.config.ts, collections, fields, hooks, access control, Payload API). Use when debugging validation errors, security issues, relationship queries, transactions, or hook behavior.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.