Author Strategy

/author-strategy — PubMed Author Strategy Analysis

Work from PubMed metadata and the title/abstract text already fetched — nothing else. Do not retrieve full text, follow external links, or resolve preprints. Signals that need citations, citation half-life, venue-impact tier, repository/preprint links, or corresponding-author role are unavailable and surface as [VERIFY] — never inferred.

Prerequisites

• Python 3.10+ with biopython, pandas, matplotlib, seaborn, and pyyaml (PyYAML is required by the archetype classifier and the rubric renderer)
• Scripts: ${CLAUDE_SKILL_DIR}/fetch_pubmed.py, ${CLAUDE_SKILL_DIR}/analyze_patterns.py, ${CLAUDE_SKILL_DIR}/pubmed_parse.py (stdlib parser), ${CLAUDE_SKILL_DIR}/classify_archetypes.py, ${CLAUDE_SKILL_DIR}/render_archetype_doc.py
• Rubric: ${CLAUDE_SKILL_DIR}/references/trajectory_archetypes.yaml (canonical) and ${CLAUDE_SKILL_DIR}/references/trajectory_archetypes.md (generated)

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}/)
4. Email for NCBI E-utilities (passed as --email)

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., "Kim DK" vs "Kim D" vs the full first name) rather than generating data.

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) and analysis_report.md with the strategy breakdown.

Study types come from the keyword rules in pubmed_parse.py, first match in this order: GBD, SR/MA, NHIS/Claims, Cross-national, National survey, Biobank, AI/ML, Clinical trial, Case report, Letter/Commentary; anything else is "Other". Present the script's labels as they are — never reclassify a paper by guess.

Step 4: Interpret and Present

Read analysis_report.md and present to the user; every count and rate comes from that report or the fetched CSV, never from memory:

1. Executive summary: total publications, growth trajectory, most frequent journals (venue-impact tier is unavailable — never present a "high-tier" rate)
2. Primary strategy: what study type dominates and why
3. Author position analysis: first/last positional rate vs middle (positional heuristic only — not leadership or corresponding-author metadata, which are unavailable here)
4. Topic clusters: research focus areas
5. ROI quadrant: which study types combine volume with first/last positional rate (chart 07; no venue-tier axis)
6. Replication opportunities: which patterns are replicable with Claude Code + public databases

State the classifier's limits when they matter: it is tuned for Korean epidemiology and public health researchers and may undercount specialized study types in other fields, and NHIS studies that lack its keywords fall into "Other".

Known limits: a supplied --orcid or --initials that contradicts every same-surname author on a paper yields match_basis orcid-conflict / initials-conflict and position unknown (a namesake is never attributed). The journal_tier CSV column always reads unavailable [VERIFY]. The A3 reporting-quality term list no longer contains claim (it matched "claims database"), so papers naming only the CLAIM checklist do not count toward dual_mode_corpus.