Skill UI

This guide provides production-ready patterns for integrating with the Palantir Foundry Platform SDK. It covers essential best practices like implementing thread-safe singletons, structured error handling, automatic pagination for large data sets, and robust retry mechanisms with exponential backoff. Ideal for refactoring existing Foundry integrations or establishing team coding standards.

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.

A comprehensive, step-by-step guide for safely upgrading the Palantir Foundry SDK. This skill covers version dependency checks, reviewing breaking changes via API changelogs, running integrated unit and integration tests, and implementing robust rollback procedures, ensuring smooth platform migration.

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 technique guides users through a rigorous two-phase research strategy. First, conduct a broad search to identify potential papers or authors. Crucially, the system instructs the user to use the extracted "author full name" as a highly specific anchor in subsequent queries. Combining the author's name with details like journal, institution, year, or biographical information ensures the most precise and reliable results, significantly narrowing down the search scope and achieving deep academic insights.

Zero-context verification system that checks every number, comparison, and scope claim in research papers against raw result files. Uses a fresh cross-model reviewer with no prior context to prevent confirmation bias. Ideal for academic integrity, paper submission, and ensuring paper-to-evidence fidelity before publication.

Generates publication-quality academic figures through a multi-stage workflow: Claude plans and reviews, while a local Codex app-server bridge renders images with native image generation. Iterates with strict scoring until the figure is paper-ready.

Search 18 scholarly APIs for papers, preprints, citations, open-access full text, repository records, and journal OA status with reproducible provenance.

Use the Parallel CLI for web search, URL/PDF extraction, deep research, data enrichment, entity discovery, and recurring web monitoring. Best when requests need current web evidence, academic sources, or change tracking.

Assess patent novelty and non-obviousness against prior art. Extracts claim elements, runs anticipation and obviousness analysis, includes cross-model examiner verification and jurisdiction-specific (USPTO/CNIPA/EPO) patentability verdicts.

Full patent drafting pipeline from invention description to jurisdiction-formatted filing documents. Supports CN, US, and EP filings, invention patents and utility models, with prior art search, claims drafting, and examiner review.

Get a multi-round external patent examiner review of a patent application, covering claims, specification, novelty, non-obviousness, clarity, enablement, and claim scope.