Skill UI

LaminDB is a Python-driven open-source framework for managing biological datasets, ensuring traceable, reproducible, FAIR data through lineage tracking, schema validation, ontology annotation, workflow integration, and lakehouse-style querying.

This workflow provides comprehensive SDK access to the Palantir Foundry Ontology. It enables users to query, filter, link, and mutate Ontology objects, and execute defined business actions. Use this skill for building sophisticated, data-driven applications that require deep interaction with structured enterprise data, including cross-object navigation and advanced server-side aggregation.

A comprehensive, step-by-step guide demonstrating how to interact with Palantir Foundry using the official Python SDK. This example covers listing ontologies, querying object types, retrieving single records by primary key, reading datasets, and applying business actions, making it ideal for developers starting a new Foundry integration or learning core API patterns.

This comprehensive guide provides step-by-step instructions for installing and configuring the Palantir Foundry SDK. It covers both the Platform SDK for direct REST API data access and the OSDK for Ontology-based workflows. Users will learn how to set up authentication using OAuth2 client credentials or Bearer tokens, ensuring secure and seamless integration into their development environment for both production and development use cases.

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 guide outlines the rigorous academic requirements for submissions to the China Sport Science journal. It covers both natural sciences (physiology, biomechanics, exercise medicine) and social sciences (sports sociology, policy, management). Submissions must address core sport science ontology problems with high methodological rigor, ensuring the contribution is central to the field, rather than merely applying other disciplines.

This skill provides a comprehensive guide for authors targeting the International Semantic Web Conference (ISWC). It details the best practices for framing semantic web, knowledge graph, and ontology papers. It helps authors ensure their manuscript meets the venue's high standards regarding semantic interoperability, real-world data application, and specific contribution types, optimizing the submission strategy.

Resolve free-text scientific labels to ontology IDs and validate existing CURIEs using EBI OLS4, Bioregistry, and Identifiers.org. Supports annotating biological fields like tissue, disease, and phenotype. Ensures metadata accuracy for submissions to GEO, ENA, and BioSamples by checking obsolescence and prefix correctness.

Start or monitor an evolutionary loop that refines ontology and acceptance criteria across generations. Supports starting new loops, monitoring status, rewinding to previous generations, and managing AI agent workflows for iterative software development. It integrates with MCP tools for ontology refinement and acceptance criteria validation.

This skill enables modeling agent mental states using BDI architecture. It transforms RDF context into beliefs, desires, and intentions, supporting cognitive reasoning and explainability in multi-agent systems. It facilitates formal cognitive structures for LLMs and tracks temporal evolution of mental states through ontology patterns.