Skill UI

A comprehensive guide to optimizing data enrichment workflows within Clay. Learn techniques to improve throughput, reduce API costs, and maximize data hit rates by correctly ordering enrichment columns, implementing conditional run rules, pre-cleaning raw leads, and managing auto-update settings. Use this when processing large datasets slowly or encountering high costs.

This skill provides comprehensive management for the entire track lifecycle within a Conductor system. It allows users to perform critical operations such as archiving, restoring, deleting, renaming tracks, and cleaning up orphaned artifacts. Use it when managing track status across active, completed, or archived states to ensure data integrity and system organization.

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

This comprehensive reference architecture details a multi-stage pipeline for web scraping and content ingestion using Firecrawl. It seamlessly integrates single-page scraping, site-wide crawling, URL mapping, and structured data extraction. The subsequent processing layer handles cleaning, deduplication, and chunking, making this blueprint ideal for designing robust knowledge bases, RAG systems, and complex AI data pipelines.

An expert guide for performing complex data analysis, cleaning, and transformation using pandas DataFrames. This skill covers advanced techniques such as vectorized operations, efficient merging, time series resampling, group-by aggregations, and memory optimization, ensuring production-grade code and robust data validation.

NeuroKit2 is a Python biosignal processing toolkit for ECG, EEG, EDA, RSP, EMG, and EOG data that automates cleaning, feature extraction, and HRV/complexity analysis for psychophysiology, clinical, and HCI studies.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

This skill guides the operation, consolidation, and maintenance of complex working systems within Google Workspace (Docs, Sheets, Slides). It emphasizes treating files as integrated systems—such as trackers or planning hubs—requiring careful inspection, precise edits, and structural cleanup rather than simple content rewriting. Use it for consolidating scattered data, cleaning shared spreadsheets, or ensuring the correct canonical version of a document is utilized.

Structured pipeline for credit risk pre-loan modeling that loads and formats raw data, filters abnormal periods, trims high-missing/low-IV/high-PSI/high-correlation features, denoises via Null Importance, and exports an Excel cleaning report for review.

This skill guides developers to finalize a development branch after testing passes. It involves verifying tests, detecting the git environment, presenting integration options (merge, PR, keep), executing the chosen action, and cleaning up worktrees. It ensures safe integration and prevents accidental data loss during branch management.

This skill guides the operation, consolidation, and maintenance of complex working systems within Google Workspace (Docs, Sheets, Slides). It emphasizes treating files as integrated systems—such as trackers or planning hubs—requiring careful inspection, precise edits, and structural cleanup rather than simple content rewriting. Use it for consolidating scattered data, cleaning shared spreadsheets, or ensuring the correct canonical version of a document is utilized.

This guide outlines the creation of a complete, verifiable research package for economic policy manuscripts. It ensures that all empirical, experimental, or simulation results are fully reproducible by providing raw data guides, cleaning scripts, analysis code, and a single 'master' script that runs end-to-end. This process is critical for academic acceptance, allowing reviewers and discussants to independently verify every figure, table, and key number reported in the paper.