taxonomy-normalizer

What This Skill Does

The taxonomy-normalizer is a specialized OpenClaw AI agent skill designed to bridge the gap between fragmented data classification systems. Organizations often struggle with inconsistent labeling across various teams, databases, and historical datasets. This skill acts as a logical architect that ingests disparate category lists, aliases, and deprecated terms to produce a unified governance structure. It excels at identifying overlaps, resolving naming conflicts, and generating clear migration paths without modifying your production environment.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/52yuanchangxing/taxonomy-normalizer

Use Cases

• Cross-Team Integration: Standardize category definitions when two departments merge their project data or customer segments.
• Data Governance Workflows: Clean up legacy data by mapping obsolete or redundant tags to a new, standardized taxonomy while maintaining historical lookup tables.
• System Migration: Prepare for a database schema update by mapping existing non-uniform categories into a new, strictly defined taxonomy framework.
• Conflict Resolution: Identify semantic overlaps where different teams are using different labels for the same logical object.

Example Prompts

1. "I have two lists of product categories from the Marketing and Engineering teams. Please analyze them, identify the overlaps, and propose a master taxonomy list."
2. "Here is a CSV containing legacy CRM status labels. Please suggest a normalization plan that keeps the old tags as aliases but moves everything to a new, clean standard."
3. "We are migrating our article tags. Please create a mapping strategy that includes a list of deprecated terms and their recommended replacements, ensuring we don't break existing historical search filters."

Tips & Limitations

• Safety First: This skill is strictly designed for planning and analysis. It will not modify your production database or external configurations. Always treat its output as a proposal for human review.
• Contextual Awareness: The skill works best when provided with context regarding the 'why' behind certain categories. If you have documentation on why certain terms are deprecated, share it to improve the accuracy of the migration plan.
• Transparency: If the skill cannot resolve a naming conflict, it will explicitly list it as a 'pending confirmation item' rather than guessing. Review these items carefully before proceeding with any data updates.
• Dry-Run Policy: Always use the provided structure to generate a 'review-only' draft before attempting to execute any data change scripts in your backend systems.