Skill UI

This guide outlines the implementation of a production-ready Snowflake data platform using the industry-standard Medallion Pattern (Bronze, Silver, Gold). It provides comprehensive, step-by-step SQL examples covering raw data ingestion (Bronze), data cleansing and typing (Silver), and business-level aggregation (Gold). Use this when designing new data warehouses or establishing robust, layered data architecture standards on Snowflake.

This comprehensive guide covers essential operations for handling PDF documents, including text, table, and metadata extraction using libraries like pdfplumber. It details document creation with reportlab, merging/splitting with pypdf/qpdf, and advanced OCR for scanned PDFs. Ideal for automating complex document workflows and structured data ingestion.

This skill extracts full transcripts and rich metadata from a single YouTube video URL. It uses yt-dlp to download subtitles, cleans VTT timing markers, and pulls video details (title, channel, date). The output is a structured, queryable Markdown file, ideal for populating a personal knowledge vault and enabling downstream knowledge graph extraction or content repurposing.

This skill automatically enriches research wiki pages that are left as bare scaffolds after initial ingestion. It reads multiple external sources (like arXiv/alphaxiv) and fills in 10 structured `_TODO._` sections (e.g., Problem/Gap, Key Results, Open Questions) with detailed prose summaries. Use this skill to transform incomplete research scaffolds into a comprehensive, structured knowledge base.

This technical skill is designed to verify the compatibility of the Codex CLI with skills compiled from MDA source. Loading this skill confirms that the CLI loader successfully parses the SKILL.md format, adhering to the agentskills.io v1 envelope subset. It is primarily used for testing build pipelines, metadata integrity, and ensuring robust compatibility between MDA compilation and runtime ingestion.

This skill generates a complete and detailed specification for an ETL/ELT data pipeline. It covers everything from source systems, transformation logic, and data quality rules to destination data warehousing and required SLAs. Use this when planning data ingestion, designing complex data flows, or needing comprehensive architecture documentation for engineering handoff.

Diagnose and accelerate large data movement pipelines by isolating bottlenecks, benchmarking variants, and codifying the fastest path with proof of correctness. Use when ingestion, backfill, ETL, or warehouse loading is too slow and must get faster without losing data integrity.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This skill facilitates the ingestion of unstructured clinical notes and reports from FHIR R4 EHR APIs (e.g., Epic, Cerner). It handles complex tasks including paginating large search results, decoding base64 attachments (PDF, text), and extracting narrative content. The extracted text is then passed to the OpenMed pipeline for HIPAA-compliant de-identification and subsequent NLP analysis, enabling structured data insights from raw clinical records.

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.

Learn to orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using tools like Prefect, Airflow, or Dagster. This skill covers scheduling recurring tasks, managing dependencies between data preparation and model serving, and building reliable, observable workflows for production AI systems.

Client library for Azure Event Hubs in Rust, enabling sending and receiving events, batch processing, and streaming data ingestion. Ideal for building scalable event-driven applications with high-throughput data pipelines.