Skill UI

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

This skill details best practices for handling and structuring Evernote data. It covers designing local database schemas (SQL), advanced ENML content processing (to Markdown/HTML), managing attachments, and implementing reliable incremental synchronization using Update Sequence Numbers (USN). Ideal for developers building robust applications requiring deep, local data access and integrity checks for Evernote content.

This guide addresses the performance degradation when automating Apple Notes, especially with large note collections (10,000+). It details architectural patterns, caching strategies, and scripting best practices (like batch processing and incremental sync) to prevent bottlenecks caused by excessive IPC calls, ensuring automation remains responsive at scale.

Provides production-grade strategies for synchronizing Navan's BOOKING and TRANSACTION data into a data warehouse. This skill covers three sync tiers: weekly full-refresh with idempotent merge-upsert logic for BOOKING, watermark-based incremental sync for TRANSACTION, and real-time webhook handling for event-driven processing. Essential for building robust, scalable data pipelines using ETL patterns.

Provides advanced patterns for high-volume Notion API interactions. Learn how to manage rate limits (3 req/sec) using worker queues, implement parallel requests with p-queue, and efficiently paginate massive databases (100K+ records) using incremental sync and chunked processing. Essential for building robust, scalable Notion automation tools.

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.

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

Build and operate CocoIndex indexing flows for AI data processing, including ETL pipelines, embeddings, vector databases, knowledge graphs, and incremental live updates.