aibot88 /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:
- Author name (PubMed format, e.g., "Kim DK" or "Lee KS")
- Last name for position classification (auto-detected if ambiguous)
- 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.mdwith strategy breakdown
Step 4: Interpret and Present
Read analysis_report.md and present to the user:
- Executive summary: total publications, growth trajectory, high-tier rate
- Primary strategy: what study type dominates and why
- Author position analysis: leadership rate (1st + last) vs middle
- Topic clusters: research focus areas
- ROI quadrant: which strategies yield high-tier + leadership vs. volume only
- 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
--emailflag).
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