Skill UI

Implement robust data governance and security measures within Palantir Foundry. This skill guides users through PII redaction, applying data markings (e.g., CONFIDENTIAL, RESTRICTED), ensuring GDPR/CCPA compliance, and establishing automated data retention policies. It provides methods for column-level security and auditable deletion workflows for sensitive information.

A structured guide for setting up a robust local development environment for Palantir Foundry data pipelines. This workflow enables users to run Python transforms and unit tests using PySpark against sample data, and mock the Foundry API client, ensuring fast, isolated iteration cycles before deploying code to the official Foundry environment. Ideal for data engineers and developers building complex ETL logic.

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 comprehensive guide details the process of implementing a secure and robust system for handling Palantir Foundry webhook events. It covers key steps including API-based webhook registration, mandatory signature verification with replay protection, sophisticated event routing for various object lifecycles (create, update, delete), and ensuring idempotent processing using caching mechanisms like Redis. Use this when external microservices must react reliably and immediately to changes within the Foundry Ontology or data assets.

This skill performs a zero-context verification audit, ensuring that every quantitative claim, comparison, and scope statement within a research paper precisely matches the provided raw result files. It is designed to eliminate confirmation bias, catching discrepancies such as incorrect rounding, cherry-picked best seeds, or misreported performance deltas before publication. Ideal for validating paper-to-evidence fidelity.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

A comprehensive workflow for scraping and extracting structured data from Google search results using the SerpApi. It captures all key components, including organic listings, knowledge graphs, answer boxes, People Also Ask (PAA), local pack results, and related searches. Ideal for SEO monitoring, market research, competitive analysis, or building search-powered features that require deep, structured data insights.

This workflow utilizes the SerpApi to perform advanced, multi-engine searches across platforms including YouTube, Bing, Google News, Google Shopping, and Google Maps. It is essential for data scraping, SEO research, and building comprehensive search features that require aggregating results from diverse sources beyond standard Google search. Use it to collect videos metadata, product pricing, local business details, and news articles efficiently.

Collects comprehensive diagnostic evidence from Snowflake's ACCOUNT_USAGE views, QUERY_HISTORY, and environment details. This tool is essential for troubleshooting persistent performance issues, connection failures, or preparing detailed support cases for Snowflake support, ensuring all necessary logs, query metrics, and resource usage data are collected in one package.

This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.

This comprehensive checklist guides the deployment of Snowflake data pipelines and integrations into a production environment. It covers critical areas such as authentication best practices, warehouse configuration, data pipeline readiness, access control, monitoring setup, and disaster recovery planning. Use it when preparing for a go-live or validating the stability of your production Snowflake setup.

A comprehensive guide to implementing production-grade reliability patterns for Snowflake environments. Covers advanced techniques like Time Travel for point-in-time recovery, cross-region database replication, account failover groups for disaster recovery, and application-level resilience coding (e.g., connection failover logic). Essential for building fault-tolerant, highly available data pipelines.