q-topic-finetuning

What problem does it solve?

This Skill transforms raw topic modeling outputs (like BERTopic or LDA) into a structured, theory-driven classification framework suitable for academic manuscripts, ensuring clarity and theoretical grounding.

Core Features & Use Cases

• Topic Consolidation: Merges numerous raw topics into a manageable set of theoretically meaningful categories.
• Theoretical Classification: Applies established frameworks (e.g., legitimacy, stakeholder theory) to categorize topics.
• Domain Preservation: Ensures that crucial domain-specific distinctions are maintained.
• Data Verification: Provides tools to verify the accuracy and completeness of the classification.
• Excel Updates: Automatically updates source data with new classification labels.
• Outlier Handling: Uses foundation models (like Gemini) to classify unassigned documents.
• Use Case: After running BERTopic on a large corpus of research papers, you have 150 topics. Use this Skill to consolidate them into 20-30 categories based on established theories of innovation, and then update your original data with these new, theory-aligned labels.

Quick Start

Use the q-topic-finetuning skill to generate an implementation plan for consolidating topic model outputs.