Author Strategy Skill for Claude

/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:

• 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.md with 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: first/last positional rate vs middle (positional heuristic only — not leadership or corresponding-author metadata, which are unavailable here)
• 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 "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:

| 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.
• 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