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.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

This guide provides a comprehensive reference architecture for building production-ready, enterprise-grade applications on Palantir Foundry. It covers the entire data lifecycle, detailing best practices for the standard data pipeline (Raw > Clean > Model), Ontology design, external API integration patterns, and robust multi-layered security models. Use it when designing, planning, or optimizing complex data infrastructure within Foundry.

This guide identifies and provides solutions for common anti-patterns and mistakes when working with Snowflake. It covers best practices in SQL writing, warehouse resource management, data loading strategies (e.g., using MERGE), and access control setup. It is essential for auditing existing deployments, onboarding new users, or performing code reviews to ensure cost efficiency and data integrity.

A static analysis tool that audits SQL code for common cost and performance anti-patterns (e.g., full-table scans, Cartesian joins). It calculates a query health score (0-100) for data warehouses like BigQuery, Snowflake, and Redshift, providing a prioritized, actionable plan with concrete fixes and estimated cost savings. Essential for FinOps and data quality assurance.

This tool scans ingested OHLCV market data to automatically detect and classify various candlestick patterns, such as dojis, engulfing patterns, and head-shoulders formations. It not only identifies the pattern name but also assigns a type (reversal/continuation), direction, and a quantitative reliability score. It is an essential utility for technical analysis, helping users identify potential market turning points or continued trends.

This plugin provides advanced vector search capabilities using two distinct paths. The large-scale path supports millions of vectors via HNSW, ideal for general corpus search. The hot-path router handles small, critical pattern routing (≤11 patterns) with sub-ms latency. Key features include RaBitQ 1-bit quantization for memory efficiency (32× reduction), hyperbolic space search for hierarchical data, and unified cross-namespace search.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

A static analysis tool that audits SQL code for common cost and performance anti-patterns (e.g., full-table scans, Cartesian joins). It calculates a query health score (0-100) for data warehouses like BigQuery, Snowflake, and Redshift, providing a prioritized, actionable plan with concrete fixes and estimated cost savings. Essential for FinOps and data quality assurance.