Skill UI

This guide provides a comprehensive workflow for building robust ETL data pipelines within Palantir Foundry. It teaches how to utilize Python transforms, including the @transform_df decorator, for dataset-to-dataset processing. Key topics covered include multi-input joins, implementing incremental updates for efficiency, low-level file I/O, and configuring Spark resources for heavy computations. Essential for data engineers working with complex data modeling in Foundry.

This skill guides the setup of comprehensive observability for Palantir Foundry integrations. It covers structured logging, Prometheus metrics (latency, errors), health checks, and Prometheus alert rules, enabling robust monitoring of API integration health, performance, and reliability. Use this when implementing production-grade monitoring for Foundry APIs.

This guide outlines comprehensive security best practices for integrating with Palantir Foundry. It covers secure credential storage (using secrets managers like AWS Secrets Manager or Vault), implementing the Principle of Least Privilege via OAuth2 scopes, and establishing robust credential rotation procedures. Use this when securing production environments, setting up new data pipelines, or auditing existing API access controls.

Access and query 10 major academic paper databases (including PubMed, arXiv, OpenAlex, Crossref, etc.) via REST APIs. This tool facilitates comprehensive scholarly literature searches, allowing users to find abstracts, check citation details (DOI/PMID), track open access status, retrieve preprints, and analyze citation graphs across diverse fields like biomedicine and physics.

This guide details best practices for securing Persona identity verification implementations. It covers critical security measures such as API key rotation, webhook HMAC verification, encrypting Personally Identifiable Information (PII) at rest, and maintaining comprehensive audit logs. Use this when integrating Persona into a production system requiring high data security and compliance.

This guide provides implementation patterns for secure integration with the Podium API using REST API calls. It focuses on establishing robust connections with OAuth2 authentication for managing core business functions, including messaging, retrieving customer reviews, and processing payments. It also details essential error handling strategies.

This skill scans live websites or local codebases to detect all data collection signals, including cookies, analytics, forms, and third-party processors. It then generates a comprehensive, 12-section privacy policy tailored for GDPR and CCPA compliance. The system maps detected data touchpoints to specific legal bases (Consent, Contract, etc.), ensuring the resulting policy accurately reflects the actual data practices of your application or website.

This skill provides implementation patterns for robust error handling when integrating with the Procore REST API. It covers essential topics like OAuth2 authentication, managing rate limiting (429), handling expired tokens (401), and resolving permission issues (403). Ideal for developers and project managers building automated workflows for construction project management, RFIs, or submittals.

Provides foundational knowledge and implementation patterns for connecting to the Procore API. This skill details how to securely implement OAuth2 authentication (using the Client Credentials flow) to manage data in critical construction processes, including project tracking, RFIs, and document submittals.

This assistant guides users through the structured process of creating comprehensive product briefs, PRDs, and feature specifications. It ensures that all critical elements—including problem statements, user value, success metrics, scope boundaries (in/out), and dependencies—are clearly defined before development begins, enabling effective product scoping and communication.

A comprehensive guide detailing how to establish robust monitoring for Qdrant. Covers setting up Prometheus metrics scraping, implementing Liveness and Readiness probes for Kubernetes, configuring alerts, and centralizing logs. It provides specific instructions for both self-hosted and Hybrid Cloud environments, ensuring full operational visibility and compliance.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.