author-strategy

name: author-strategy description: PubMed author profile analysis. Author name → PubMed fetch → study-type classification → visualization → strategy report → optional trajectory-archetype classification. triggers: author-strategy, 저자 분석, publication analysis, 다작 분석, 연구 전략 분석, author profile, reverse engineer strategy, trajectory archetype, career archetype tools: Read, Write, Edit, Bash, Glob, Grep model: inherit

/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, 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}/)

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: 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 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 "what MA topics are feasible with this professor?":

• 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

Optional: Trajectory-Archetype Classification

A second, opt-in capability that classifies the author's trajectory into abstract career archetypes (A1–A6 + a composite) as an explainable, multi-label, confidence-scored heuristic — not an objective verdict. The rubric is the canonical references/trajectory_archetypes.yaml. This path is gated: a surname alone does not resolve an author, so the corpus must pass an explicit disambiguation review before it can be classified.

Step 6: Disambiguation Gate (required before classification)

Pass disambiguators so the target author is uniquely attributed (a surname alone is never sufficient):

python "${CLAUDE_SKILL_DIR}/fetch_pubmed.py" "{Author Name}" \
  --initials "{Initials}" --orcid "{ORCID}" \
  --affiliation "{Institution}" --year-from "{YYYY}" --year-to "{YYYY}" \
  --output "{output_dir}/data/{name}_publications.csv" --email "{user_email}"

This writes the CSV, a candidates.json of affiliation/year candidate clusters, and a corpus_manifest.json with review_status: pending. Present the candidate clusters to the user for review. The user decides include/exclude. Only after the user has reviewed the clusters do you finalize and approve the corpus (the --approve flag is a human gate — never set it without explicit user review/approval):

python "${CLAUDE_SKILL_DIR}/fetch_pubmed.py" "{Author Name}" \
  --initials "{Initials}" --affiliation "{Institution}" \
  --include-pmids "{included.txt}" --exclude-pmids "{excluded.txt}" --approve \
  --output "{output_dir}/data/{name}_publications.csv" --email "{user_email}"

The manifest is cryptographically bound to the CSV (csv_sha256 + pmid_set_hash); the classifier refuses to run on an unapproved or mismatched corpus.

Step 7: Run the Classifier and Present

python "${CLAUDE_SKILL_DIR}/classify_archetypes.py" \
  "{output_dir}/data/{name}_publications.csv" \
  --manifest "{output_dir}/data/corpus_manifest.json" \
  --rubric "${CLAUDE_SKILL_DIR}/references/trajectory_archetypes.yaml" \
  --output-dir "{output_dir}/report/"

Read archetype_report.md and present it to the user, stating up front that the labels are explainable heuristics, not objective classifications. For each surfaced archetype, show the score, confidence band, and the author's own evidence PMIDs. Honor the [VERIFY] markers (h-index/citation/venue-tier are unavailable) and the A5 participation flag. List the insufficient evidence archetypes too.

To retune the rubric, edit only the YAML and regenerate the narrative doc:

python "${CLAUDE_SKILL_DIR}/render_archetype_doc.py"        # regenerate the .md
python "${CLAUDE_SKILL_DIR}/render_archetype_doc.py" --check # CI/test sync gate

Study Type Classifier

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

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.
• Archetype labels are explainable heuristics, not objective classifications. Every label must carry a score, a confidence band, and evidence (the queried author's own PMIDs). Below the minimum sample or with conflicting signals, report insufficient evidence — never force a label.
• Metadata + stored abstract only. Signals are computed from PubMed metadata and the title/abstract text already fetched. 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.
• Author position is a positional heuristic (first/middle/last/unknown + real EqualContrib). Never present it as authoritative leadership or corresponding-author metadata.
• Never resolve an author by surname alone. Classification requires an approved, CSV-bound corpus_manifest.json; present candidate clusters for the user to confirm.

Output Structure

{output_dir}/
  data/
    {name}_publications.csv
    candidates.json          # disambiguation candidate clusters (Step 6)
    corpus_manifest.json     # review_status + csv_sha256 + pmid_set_hash (Step 6)
  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
    archetype_report.md      # trajectory-archetype classification (Step 7)
    archetype_results.json   # machine-readable labels + scores + evidence