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Found 45 Skills
Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
Ingest and transform large data files (CSV/JSON) into Elasticsearch indices. Stream-based processing for files up to 30GB, cross-version migration (ES 8.x ↔ 9.x), custom JavaScript transformations, and reindexing with transforms. Use when you need to load data into Elasticsearch, migrate indices, or transform data during ingestion.
JSON querying, filtering, and transformation with jq command-line tool. Use when working with JSON data, parsing JSON files, filtering JSON arrays/objects, or transforming JSON structures.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Pandas for time series analysis, OrcaFlex results processing, and marine engineering data workflows
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Integrate external APIs and services with error handling, retry logic, and data transformation. Use when connecting to payment processors, messaging services, analytics platforms, or other third-party providers.
Consult this skill when designing data pipelines or transformation workflows. Use when data flows through fixed sequence of transformations, stages can be independently developed and tested, parallel processing of stages is beneficial. Do not use when selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: data flow is not sequential or predictable. DO NOT use when: complex branching/merging logic dominates.
Create and manage Infrahub transforms. Use when building data transformations, config generation, or any workflow that converts Infrahub data into a different format (JSON, text, CSV, device configs) using Python or Jinja2 templates.
Use when analyzing FileMaker DDR to extract calculations, custom functions, and business logic for PostgreSQL import processes or maintenance scripts - focuses on understanding and adapting FileMaker logic rather than direct schema migration
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.