q-scholar
Q-Scholar
Q-Scholar is an overarching academic writing skill that orchestrates specialized sub-skills to support the complete manuscript preparation workflow. It produces journal-ready prose following APA 7th edition standards.
Sub-Skills
q-intro
Introduction drafting and refinement with argumentative architecture guidance. Supports two modes: interview-based drafting from scratch, and diagnostic refinement of existing drafts. Produces flowing prose that establishes context through a single narrative arc, identifies literature gaps as natural consequences of the trajectory, motivates theoretical frameworks as resolutions to identified needs, and frames research questions with clear scope progression.
Use for: Writing or refining introduction sections that move from phenomenon to theory to empirical contribution, with discipline-first literature grounding, cross-paragraph bridge architecture, and enumerated contributions.
q-exploratory-analysis
Universal exploratory data analysis for tabular datasets. Interviews the user to confirm column measurement levels (Nominal, Ordinal, Discrete, Continuous, Temporal, Text) and applies statistically appropriate analysis for each type. Produces a structured TABLE/ folder of CSV outputs and a holistic EXPLORATORY_SUMMARY.md with flagged insights.
Use for: Initial data exploration, measurement-level-appropriate descriptive statistics, understanding dataset structure before formal analysis.
q-topic-finetuning
Fine-tune and consolidate topic modeling outputs (BERTopic, LDA, NMF) into theory-driven classification frameworks for academic manuscripts. Handles topic consolidation, theoretical classification, domain-specific preservation, multi-category assignments, and Excel label updates.
Use for: Converting 50+ raw topics into 20-50 manuscript-ready categories, applying theoretical frameworks (legitimacy, stakeholder theory, sentiment) to topic clusters, generating reproducible Excel outputs with classification labels.
q-methods
Methods section drafting in clear, narrative style. Produces flowing paragraphs organized by workflow stages with appropriate appendix cross-references for technical details. Maintains strict separation between methods and results: describes procedures and summarizes data collected, but reserves analysis findings for the results section.
Use for: Writing data collection, preprocessing, analysis procedures, validation descriptions, and corpus summaries (sample size, date range, category distributions).
q-results
Results section drafting following APA 7th edition guidelines. Produces narrative prose organized by research questions with properly formatted tables.
Use for: Presenting findings, formatting statistical results, creating APA-compliant tables.
Core Writing Principles
Across all sub-skills, Q-Scholar maintains consistent standards:
- Narrative prose over bullet points
- No em-dashes; use hyphens for compound modifiers only
- No unnecessary bold or italic emphasis
- APA 7th statistical notation (italicized symbols)
- Numbers: spell out below 10 unless measurements/statistics
- Tables: APA format with number, title, notes
- Appendices for technical details and comprehensive codebooks
- Placeholders for missing information (coauthor contributions, pending metrics)
Usage Patterns
Full Manuscript Support
User: Help me write the introduction, methods, and results for my topic modeling study
Assistant: [Invokes q-intro, q-methods, then q-results in sequence]
Targeted Section Drafting
User: Draft an introduction for my paper on esports legitimacy
Assistant: [Invokes q-intro with interview workflow]
Data Exploration First
User: I have a new dataset and need to understand it before writing
Assistant: [Invokes q-exploratory-analysis for exploration, then proceeds to writing sections]
Quality Standards
All output should meet these criteria:
- Ready for submission to peer-reviewed journals
- Consistent formatting throughout
- Complete information (or explicit placeholders)
- Appropriate use of appendices
- Logical organization and flow
- Objective reporting without premature interpretation
Folder Structure
q-scholar/
├── SKILL.md # This file (orchestration)
├── references/ # Shared style guides
│ ├── apa_style_guide.md # Numbers, statistics, notation
│ └── table_formatting.md # APA 7th table examples
├── q-intro/
│ ├── SKILL.md # Introduction drafting skill
│ └── references/
│ ├── introduction_template.md
│ └── interview_questions.md
├── q-exploratory-analysis/
│ ├── SKILL.md # Data exploration skill
│ ├── scripts/
│ │ ├── run_eda.py # Six-phase EDA runner
│ │ └── requirements.txt # Python dependencies
│ └── references/
│ └── summary_template.md # 13-section summary template
├── q-topic-finetuning/
│ ├── SKILL.md # Topic modeling consolidation skill
│ ├── scripts/
│ │ ├── classify_outliers.py # Outlier reclassification via Gemini
│ │ ├── generate_implementation_plan.py
│ │ └── update_excel_with_labels.py
│ └── references/
│ ├── esports_ugc_example.md # Worked example
│ └── SP_OUTLIER_TEMPLATE.txt # Outlier classification prompt
├── q-methods/
│ ├── SKILL.md # Methods drafting skill
│ └── references/
│ ├── methods_template.md
│ └── appendix_template.md
└── q-results/
├── SKILL.md # Results drafting skill
└── references/
└── results_template.md
Cross-References
Shared references (apply to all sub-skills):
- references/apa_style_guide.md: numbers, statistics, notation
- references/table_formatting.md: APA 7th table examples
Sub-skill specific references:
- q-intro/references/: introduction_template.md, interview_questions.md
- q-exploratory-analysis/SKILL.md: workflow templates, script invocation
- q-exploratory-analysis/scripts/run_eda.py: six-phase EDA pipeline
- q-topic-finetuning/references/: esports_ugc_example.md, SP_OUTLIER_TEMPLATE.txt
- q-topic-finetuning/scripts/: classify_outliers.py, generate_implementation_plan.py, update_excel_with_labels.py
- q-methods/references/: methods_template.md, appendix_template.md
- q-results/references/: results_template.md