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Found 44 Skills
Complete guide for dbt data transformation including models, tests, documentation, incremental builds, macros, packages, and production workflows
Transform data between JSON, CSV, and other formats with filtering, mapping, and flattening. Use when: (1) Converting API responses to CSV, (2) Processing data pipelines, (3) Extracting specific fields, or (4) Flattening nested structures.
Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes. Use for analytics pipelines, data transformations, and data modeling.
Write JavaScript code in n8n Code nodes. Use when writing JavaScript in n8n, using $input/$json/$node syntax, making HTTP requests with $helpers, working with dates using DateTime, troubleshooting Code node errors, or choosing between Code node modes.
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify.
Extracts specific fields from JSON files efficiently using jq instead of reading entire files, saving 80-95% context. Use this skill when querying JSON files, filtering/transforming data, or getting specific field(s) from large JSON files
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.
TransForm integration. Manage data, records, and automate workflows. Use when the user wants to interact with TransForm data.
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.