storage-classify

What problem does it solve?

Without consistent routing rules, data artifacts like validated SQL, business facts, and metric definitions end up in inconsistent or incorrect storage, making them hard to find, breaking validation rules, and reducing reusability across data projects.

Core Features & Use Cases

• Intelligent Artifact Classification: Automatically categorizes content into 7 distinct persistent stores (semantic_models, metrics, reference_sql, knowledge, memory, AGENTS.md, skills) based on its type, lifetime, and reuse pattern.
• Strict Routing Enforcement: Follows project-defined routing rules, such as delegating semantic model generation to dedicated subagents instead of hand-writing YAML, and ensuring one SQL query per reference_sql entry for accurate indexing.
• Ambiguity Handling: Prompts the user for clarification when content does not clearly fit a store or routing would cause destructive overwrites, preventing accidental data loss. Use Case: When a data engineer finishes validating a new sales metric SQL query, this skill routes it to the reference_sql store via the gen_sql_summary subagent, passes the associated business context to ensure the query is searchable for future reuse, and indexes it correctly in the vector store.

Quick Start

Use the storage-classify skill to route the newly validated customer churn metric definition to the correct persistent storage location.