/author-strategy

name: author-strategy
description: Use when analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author position, charts the patterns and writes a strategy report, with an optional trajectory-archetype classification. Works from PubMed metadata only.
metadata:
  triggers: "author-strategy, 저자 분석, publication analysis, 다작 분석, 연구 전략 분석, author profile, reverse engineer strategy, trajectory archetype, career archetype"

/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`.

Step 5: Optional — MA Gap Identification

If the user asks "what MA topics are feasible with this professor?":

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

Optional: Trajectory-Archetype Classification

An opt-in path that classifies the trajectory into abstract career archetypes (A1–A6 + a composite) as an **explainable, multi-label, confidence-scored heuristic — not an objective verdict**, using the canonical rubric `references/trajectory_archetypes.yaml`.

Step 6: Disambiguation Gate (required before classification)

A surname alone never resolves an author. Pass disambiguators so the target author is uniquely attributed:

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-pmi