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Found 33 Skills
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.
Structured data extraction from web pages using claude-in-chrome MCP with sequential-thinking planning. Focus on READ operations, data transformation, and pagination handling for multi-page extraction.
Convert between physical units (length, mass, temperature, time, etc.). Use for scientific calculations, data transformation, or unit standardization.
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
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.
Automated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines. Processes data sourced from BigQuery or Cloud Storage (GCS), applying best practices for data ingestion, movement, schema mapping, and comprehensive data cleaning.
Coaches users to transform messy data into clean, analysis-ready formats using Power Query UI. Diagnoses data problems, visualizes goals, and guides step-by-step transformations.
Process large datasets efficiently using chunk(), chunkById(), lazy(), and cursor() to reduce memory consumption and improve performance