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q-topic-finetuning

Consolidate BERTopic or LDA topics into theory-driven classifications for academic manuscripts.

24|1|Updated Jan 29, 2026
Search Tags:#research#data analysis#classification#topic modeling#academic writing#BERTopic#LDA
One-click install
npx skills add https://github.com/TyrealQ/q-skills --skill q-topic-finetuning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: q-topic-finetuning
Source: https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-topic-finetuning
Command: npx skills add https://github.com/TyrealQ/q-skills --skill q-topic-finetuning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about q-topic-finetuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I consolidate raw BERTopic or LDA outputs into a theory-driven classification framework?▼

The topic consolidation process works by merging raw BERTopic or LDA outputs into a manageable set of theoretically meaningful categories, applying established frameworks like legitimacy or stakeholder theory to ensure theoretical grounding.

Can I use foundation models to classify outlier documents not assigned during topic modeling?▼

Yes, foundation models handle outlier classification for unassigned documents. This Skill uses models like Gemini to categorize outlier documents that were not assigned during the initial topic modeling phase, ensuring complete data coverage.

How do I apply stakeholder theory to categorize raw topics for academic manuscripts?▼

You apply theoretical classification by mapping raw topics to established frameworks like stakeholder theory. This ensures academic manuscripts maintain domain-specific distinctions while grounding the consolidated categories in relevant theoretical concepts.

Does this topic consolidation approach automatically update Excel data with new classification labels?▼

Yes, Excel updates are automated. The Skill directly updates your source spreadsheet data with the final theory-aligned classification labels, ensuring your original dataset reflects the newly consolidated and theoretically grounded categories.

What is the best way to refine topic models into theory-driven categories for academic writing?▼

The best way is to consolidate topic model outputs into a theory-driven classification framework. This process handles domain-specific preservation, multi-category assignments, and data verification, ensuring raw topics are refined into theoretically meaningful categories.

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