Knowledge Base Classification

The problem

Knowledge bases often devolve into flat document piles where nothing is findable. This lead to duplicate work as team members create new documents rather than searching for existing ones.

What it does

• Measures the document store before and after to quantify organization improvements.
• Applies hierarchical classification rules where earlier rules take priority to ensure reproducibility.
• Identifies and flags ambiguous documents instead of forcing them into incorrect categories.
• Validates all parent-child relationships to prevent circular dependencies and excessive nesting depth.
• Normalizes document titles to fix character-level inconsistencies that break search functionality.

Why this beats prompting it yourself

General prompts often "hallucinate" categories for documents that don't fit, creating a messy junk drawer. This skill enforces strict rule ordering and cycle detection that manual prompting misses, ensuring your file structure remains technically sound.

Use cases

• Converting a flat folder of 500+ mixed documents into a client-project hierarchy.
• Identifying and routing stale content to a reversible archive instead of permanent deletion.
• Restructuring a knowledge base after a company merger or project pivot.
• Cleaning up inconsistent naming conventions across shared documentation drives.

Known limitations

Requires an initial taxonomy of clients, projects, or categories to be provided by the user or existing project metadata.

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