Skill UI

This comprehensive guide identifies and explains common anti-patterns, gotchas, and integration mistakes when using Clay's data enrichment platform. It details the root causes of credit waste, workflow failures, and data loss, providing actionable fixes for auditing configurations, onboarding new team members, or optimizing existing pipelines.

Provides comprehensive monitoring for the Clay data enrichment pipeline. It tracks critical operational metrics such as credit consumption velocity, enrichment success rates (hit rates), data quality scores, and lead distribution. Essential for cost control and ensuring pipeline health in data-intensive CRM workflows.

A comprehensive system for implementing policy guardrails in data enrichment pipelines. It enforces critical controls including credit spending limits, blocks the enrichment of sensitive PII (e.g., SSN, bank details), and validates input data quality. Essential for maintaining compliance (GDPR/CCPA) and managing costs when processing large volumes of personal and business data.

This comprehensive checklist guides users through validating and preparing Clay enrichment pipelines for a production launch. It covers critical areas including table schema finalization, data quality gates (validation, deduplication), credit cost management, webhook reliability testing, and establishing robust monitoring and alerting procedures before going live.

Query the STRING REST API to retrieve protein-protein interaction networks, map identifiers, visualize evidence-weighted edges, and run GO/KEGG/Pfam enrichment for systems biology analysis.

This system automates the tracking and mapping of threat actor Command-and-Control (C2) networks. By leveraging passive DNS records, WHOIS data, certificate transparency logs, and IP enrichment, it helps security analysts perform infrastructure pivoting. It identifies shared hosting patterns, monitors domain reuse, and continuously maps adversary relationships for proactive threat intelligence.

This skill provides a systematic framework for managing Indicators of Compromise (IOCs) throughout their entire life cycle—from initial discovery and validation to enrichment, deployment, monitoring, and eventual retirement. It emphasizes implementing confidence decay functions, tracking hit rates, and managing false positives to maintain a high-quality, actionable threat intelligence database, thereby minimizing analyst fatigue and maximizing detection efficacy.

An all-in-one toolkit designed for advanced web tasks, including web searching, content extraction from URLs, batch data enrichment, and deep, comprehensive research reporting. It prioritizes academic and scientific sources for accurate and authoritative results, making it ideal for academic, scientific, and professional data analysis workflows.

This skill provides an end-to-end, highly reproducible workflow for bulk RNA-seq differential expression analysis. It orchestrates the entire pipeline, guiding users from raw FASTQ reads through Quality Control (QC), trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and finally executing differential expression testing (pydeseq2) and pathway enrichment analysis. It is designed for robust, statistically sound, and fully documented results.

A comprehensive tool for performing pathway and gene-set enrichment analysis (GSEA, ORA, ssGSEA, GSVA). Used in computational biology to determine which biological pathways or gene sets are significantly over-represented in a given list of genes or ranked gene data. Supports multiple databases (GO, KEGG, Reactome, MSigDB) and provides tools for result interpretation, visualization, and publication-ready reporting.