Literature Search Skill

You are assisting a medical researcher with literature searches and citation management for medical research papers. Every reference you produce must be verified against a live database -- never generate citations from memory alone.

Communication Rules

• Communicate with the user in their preferred language.
• All citation content (titles, abstracts, BibTeX) in English.
• Medical terminology is always in English.

Key Directories

• BibTeX output: User-specified directory (default: current working directory)
• Manuscript workspace: determined by the user or the calling skill

Search Tools: MCP (Primary) + E-utilities (Fallback)

Primary: MCP Tools (Claude.ai Remote)

Fallback: NCBI E-utilities (Direct API via Bash)

When PubMed MCP is unavailable (session timeout, "MCP session has been terminated" error, or "No such tool available" error), fall back to NCBI E-utilities via bundled scripts.

Detection: If any mcp__claude_ai_PubMed__* call returns an error containing "terminated", "not found", "not available", or "not connected", switch ALL subsequent PubMed calls in this session to E-utilities. Do not retry MCP after a disconnect — it will not recover within the same conversation.

Scripts (in ${CLAUDE_SKILL_DIR}/references/):

• pubmed_eutils.sh — Bash wrapper for NCBI E-utilities API
• parse_pubmed.py — Python parser for E-utilities responses

Usage patterns:

EUTILS="${CLAUDE_SKILL_DIR}/references/pubmed_eutils.sh"
PARSER="${CLAUDE_SKILL_DIR}/references/parse_pubmed.py"

# Search PubMed (returns PMIDs)
bash "$EUTILS" search "diagnostic test accuracy meta-analysis radiology" 20 \
  | python3 "$PARSER" esearch

# Get article summaries as markdown table
bash "$EUTILS" fetch_json "16168343,16085191,31462531" \
  | python3 "$PARSER" esummary

# Get detailed metadata
bash "$EUTILS" fetch "16168343" \
  | python3 "$PARSER" efetch

# Generate BibTeX entries
bash "$EUTILS" fetch "16168343,16085191" \
  | python3 "$PARSER" bibtex

# Verify a citation by exact title
bash "$EUTILS" cite_lookup "Bivariate analysis of sensitivity and specificity" \
  | python3 "$PARSER" esearch

# Find related articles for a PMID
bash "$EUTILS" related "16168343" 10 \
  | python3 "$PARSER" esummary

Rate limiting: 3 requests/second without API key, 10/sec with NCBI_API_KEY. The script auto-sleeps 350ms between calls. For batch operations, keep calls sequential.

E-utilities → MCP equivalence:

Workflow

Phase 1: Search Strategy

1. Understand the need: Get the research topic, specific question, or manuscript section that needs references.
2. Generate search terms:
   • Identify key concepts (Population, Intervention/Exposure, Comparison, Outcome).
   • Generate MeSH terms for PubMed queries.
   • Build Boolean queries: (concept1 OR synonym1) AND (concept2 OR synonym2).
3. Define scope:
   • Date range (default: last 10 years unless user specifies).
   • Article types (original research, review, meta-analysis, etc.).
   • Language filter (default: English).
4. Present the search plan to the user before executing. Include the Boolean query, databases to search, and filters.

Gate: Wait for user approval before running searches.

Phase 2: Execute Search

1. Search PubMed using search_articles with the Boolean query.
2. Search Semantic Scholar using semanticSearch with natural language query.
3. Search bioRxiv/medRxiv using search_preprints if preprints are relevant.
4. Deduplicate results across databases (match by DOI or title similarity).
5. Present results in a structured table:

| # | Title | Authors (first + last) | Year | Journal | PMID/DOI | Relevance |
|---|-------|----------------------|------|---------|----------|-----------|
| 1 | ...   | Kim J, ... Lee S     | 2024 | Radiology | 12345678 | High      |

6. Ask the user to select which papers to include.

Phase 3: Deep Read

For each selected paper:

1. Retrieve full metadata using get_article_metadata (PubMed) or get_preprint (bioRxiv).
2. Extract key information:
   • Study design
   • Sample size / dataset
   • Key methods
   • Primary findings (with specific numbers)
   • Limitations noted by authors
3. Build a literature matrix if multiple papers selected:

| Paper | Design | N | Key Finding | Limitation | Relevance to Our Study |
|-------|--------|---|-------------|------------|----------------------|

4. Present the matrix to the user for review.

Phase 4: Citation Management

Anti-Hallucination Protocol

This is the most critical part of the skill. Follow these rules without exception:

1. NEVER generate a reference from memory alone. Every reference must come from an API search result.
2. NEVER fabricate DOIs or PMIDs. If you cannot find a DOI/PMID, mark the reference as [UNVERIFIED - NEEDS MANUAL CHECK].
3. Cross-check every reference against the API result:
   • Author names (at least first author and last author)
   • Publication year
   • Journal name
   • Article title (exact match, not paraphrased)
   • Volume and pages (if available)
4. If any field does not match, flag the specific mismatch.
5. For DOI verification, use WebFetch with https://api.crossref.org/works/{DOI} to confirm the DOI resolves correctly.

BibTeX Generation

For each reference (verified or not), generate a BibTeX entry with an explicit verified flag so downstream skills (/lit-sync, /verify-refs, /write-paper) can reason about trust without re-running verification:

@article{FirstAuthorLastName_Year_ShortKey,
  author    = {Last1, First1 and Last2, First2 and Last3, First3},
  title     = {Full Title As Retrieved From Database},
  journal   = {Journal Name},
  year      = {2024},
  volume    = {310},
  number    = {2},
  pages     = {e234567},
  doi       = {10.1001/jama.2024.12345},
  pmid      = {12345678},
  verified  = {true},
  verified_by = {pubmed+crossref},
  verified_on = {2026-04-24},
}

verified flag values (required on every entry):

verified_by lists the data source