author-strategy

/author-strategy — PubMed Author Strategy Analysis

Purpose

Analyze a researcher's PubMed publication portfolio to reverse-engineer their research strategy. Produces a CSV dataset, 7 visualizations, and a strategy report.

Prerequisites

• Python 3.10+ with biopython, pandas, matplotlib, seaborn
• Scripts: ${CLAUDE_SKILL_DIR}/fetch_pubmed.py, ${CLAUDE_SKILL_DIR}/analyze_patterns.py

Workflow

Step 1: Gather Input

Ask the user for:

1. Author name (PubMed format, e.g., "Kim DK" or "Lee KS")
2. Last name for position classification (auto-detected if ambiguous)
3. Output directory (default: ~/.local/cache/author-strategy/{AuthorName}/)

Step 2: Fetch PubMed Data

python "${CLAUDE_SKILL_DIR}/fetch_pubmed.py" "{Author Name}" \
  --last-name "{LastName}" \
  --output "{output_dir}/data/{name}_publications.csv" \
  --email "{user_email}"

Review the console summary (total count, study type distribution, author position). If count is 0, suggest alternative name formats (e.g., "Yon DK" vs "Yon D" vs "Yon Dong Keon").

Step 3: Generate Visualizations and Report

python "${CLAUDE_SKILL_DIR}/analyze_patterns.py" "{output_dir}/data/{name}_publications.csv" \
  --output-dir "{output_dir}/report/" \
  --author-name "{Author Name}"

This produces:

• 7 PNG charts (01-07)
• analysis_report.md with strategy breakdown

Step 4: Interpret and Present

Read analysis_report.md and present to the user:

1. Executive summary: total publications, growth trajectory, high-tier rate
2. Primary strategy: what study type dominates and why
3. Author position analysis: leadership rate (1st + last) vs middle
4. Topic clusters: research focus areas
5. ROI quadrant: which strategies yield high-tier + leadership vs. volume only
6. Replication opportunities: which patterns are replicable with Claude Code + public databases

Step 5: Optional — MA Gap Identification

If the user asks "이 교수님과 MA 가능한 주제?":

• Cross-reference topic clusters with existing MA plans in memory
• Identify gaps where the professor has domain expertise but no MA published
• Output a prioritized list of MA proposals

Study Type Classifier

The classifier is tuned for Korean epidemiology and public health researchers. Categories:

| Type | Detection Pattern | |------|------------------| | GBD | "global burden" or "gbd" in title/abstract | | SR/MA | "systematic review" or "meta-analysis" | | NHIS/Claims | "national health insurance", "nhis", "claims database", "nationwide cohort" | | Cross-national | Country pairs or "cross-national"/"binational" | | National survey | "knhanes", "nhanes", "kchs", "national survey" | | Biobank | "biobank" | | AI/ML | "machine learning", "deep learning", "artificial intelligence" | | Clinical trial | "randomized" or publication type | | Case report | "case report" | | Letter/Commentary | Publication type = letter/comment/editorial |

Known limitation: The classifier may undercount NHIS studies when they appear in Cross-national or Other categories. The report notes this.

Known Limitations

• The study type classifier is tuned for epidemiology and public health researchers. May undercount specialized study types for other fields.
• NHIS studies may be undercounted when they appear in cross-national or "other" categories.
• PubMed search requires an email for NCBI E-utilities (set via --email flag).

Anti-Hallucination

• Never fabricate publication counts, h-index, or journal metrics. All numbers must come from PubMed API output.
• Never invent study classifications. If a paper cannot be classified, label it as "Other" rather than guessing.
• If PubMed returns 0 results, suggest alternative name formats rather than generating fake data.

Output Structure

{output_dir}/
  data/
    {name}_publications.csv
  report/
    analysis_report.md
    01_yearly_stacked.png
    02_study_type_pie.png
    03_author_position.png
    04_journal_tier_heatmap.png
    05_topic_distribution.png
    06_growth_curve.png
    07_strategy_roi.png