Skill UI

This guide provides production-ready patterns for integrating with the Notion API using official SDKs for TypeScript and Python. It covers advanced techniques including client initialization, executing complex database queries with filters and sorts, implementing cursor-based pagination for large datasets, constructing and manipulating rich text blocks, and ensuring robust, type-safe error handling.

A comprehensive tool for interacting with Notion via its API. It allows users to perform workspace-wide searches, run complex queries against structured databases using compound filters (AND/OR), retrieve specific pages, and deeply traverse and extract all nested block content. Supports pagination for handling large datasets, enabling full data visibility and structured data extraction.

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 guide teaches best practices for configuring robust, enterprise-grade Role-Based Access Control (RBAC) within Palantir Foundry. Learn how to define project roles (Viewer, Editor, Owner), manage organization-level groups to enforce least privilege, establish secure service user accounts for external integrations, and utilize marking-based data classification to ensure data governance and security across large datasets.

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 guide provides comprehensive strategies for scaling datasets that exceed the capacity of a single node. It covers various architectural patterns, including multi-tenancy with payload partitioning and sliding time window management. We detail the full scaling progression, from optimizing a single node (Vertical Scaling) to distributing data across multiple nodes via sharding (Horizontal Scaling), ensuring reliable handling of massive, growing datasets.

This guide provides advanced techniques to optimize interactions with the SalesLoft REST API v2. Key strategies include implementing LRU caching for frequently accessed data, utilizing incremental sync with `updated_at` filtering to avoid costly deep pagination, configuring connection pooling for stability, and running parallel reads safely. Use this when experiencing high latency, managing large datasets, or minimizing API usage costs for bulk operations.

This comprehensive benchmark suite evaluates the performance of Vision-Language Models (VLM) on complex video anomaly detection tasks using the SmartHome-Bench dataset. It covers a wide range of real-world smart home scenarios, including security breaches, elderly fall detection, and unusual pet behavior. Unlike single-frame analysis, this benchmark requires deep multi-frame video understanding, making it a robust tool for testing the advanced AI capabilities of video monitoring systems.

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.

A comprehensive resource hub for ML researchers working in scientific domains such as biology, chemistry, genetics, and medicine. It provides a curated catalog of high-signal datasets, pre-trained models, and interactive demos, streamlining the discovery and use of specialized AI/ML resources beyond general search.

This skill helps discover new Japanese Natural Language Processing (NLP) resources, including GitHub repositories and Hugging Face models/datasets, that are not yet documented in the awesome-japanese-nlp-resources list. It analyzes a given topic, suggests candidates using web search, and outputs contribution-ready markdown for easy integration.

Combines a curated Japanese NLP resource dataset with live web research to produce an up-to-date trend report on models, tooling, benchmarks, embeddings, speech, and related subfields.