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Consolidate topic modeling outputs (BERTopic, LDA, NMF) into theory-driven classification frameworks. Use for topic finetuning, topic consolidation, reclassification, outlier handling, or updating Excel labels from topic models.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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| name | q-tf |
| description | Consolidate topic modeling outputs (BERTopic, LDA, NMF) into theory-driven classification frameworks. Use for topic finetuning, topic consolidation, reclassification, outlier handling, or updating Excel labels from topic models. |
Fine-tune topic modeling outputs into consolidated, theory-driven topic frameworks for academic manuscripts.
If in plan mode: write a brief plan — "Run q-tf skill: load topic model output, define final topic structure with theoretical framework, generate implementation plan, update Excel with labels." — then exit plan mode immediately. Do NOT attempt topic analysis, script execution, or Excel updates while plan mode is active.
Agent execution instructions:
SKILL_DIR.${SKILL_DIR}/scripts/<script-name>.${SKILL_DIR}/references/<ref-name>.pandas
openpyxl # required for .xlsx input/output
google-genai # required for outlier classification via Gemini
Install: pip install pandas openpyxl google-genai
Environment variables: GEMINI_API_KEY (for outlier classification only), GEMINI_MODEL (optional model override).
| Step | Action | Reference |
|---|---|---|
| 1 | Load & analyze topics — identify overlaps, unassigned | — |
| 2 | Define final topic structure (FINAL_TOPICS dictionary) | references/code_patterns.md |
| 3 | Apply theoretical framework — classify each topic | references/preservation_rules.md |
| 4 | Generate implementation plan (MD) | scripts/generate_implementation_plan.py |
| 5 | Update source data with labels (Excel) | scripts/update_excel_with_labels.py |
| 6 | Reclassify outliers via foundation model | references/outlier_workflow.md |
python "${SKILL_DIR}/scripts/generate_implementation_plan.py" --input topic_model_output.xlsx --output implementation_plan.md
python "${SKILL_DIR}/scripts/update_excel_with_labels.py" --input document_data.xlsx --output document_data_labeled.xlsx
Adapt scripts by updating FINAL_TOPICS, FINAL_LABELS, and theme categories. See references/code_patterns.md. For a worked example, see references/esports_ugc_example.md.
| Output | Description |
|---|---|
implementation_plan.md | Full classification plan with topic mappings and reconciliation |
*_labeled.xlsx | Source data with Final_Topic_Code, Final_Topic_Label, Category_Theme columns |
| Outlier results (optional) | Updated Final_Topic_Label, classification_confidence, key_phrases columns |
Include: Topic consolidation, theoretical classification, Excel label updates, outlier reclassification. Exclude: Topic modeling itself (BERTopic/LDA/NMF execution), visualization, statistical analysis.