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Found 41 Skills
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.
Analyze and transform CSV data using bash tools
This spell is about representation change, not: - Naming changes (same structure, different identifiers) - Execution changes (same code, different runtime) - Architecture changes (new system design) - Duplication (same thing, different place) The key test: Can you point to a source artifact and a target artifact where the same information is expressed in structurally different ways? If yes → Polymorph.
Master of the Modern Utility Toolbelt, specialized in AI-enhanced CLI, structured data transformation, and advanced Unix forensics.
Prefect Flow Builder - Auto-activating skill for Data Pipelines. Triggers on: prefect flow builder, prefect flow builder Part of the Data Pipelines skill category.
Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing, custom transforms, and cross-version reindexing. Use when loading files, batch importing data, or migrating indices across versions — not for general ingest pipeline design or bulk API patterns.
TransForm integration. Manage data, records, and automate workflows. Use when the user wants to interact with TransForm data.
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.
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.
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows