Fulltext Retrieval Skill

Batch download open-access full-text PDFs from a DOI list using legitimate OA APIs only.

Pipeline

DOI → arXiv (10.48550/arXiv.* DOIs) → Unpaywall → PMC (Europe PMC / OA FTP / web) → OpenAlex → Crossref → landing page

Each DOI goes through these sources in order until a valid PDF (≥10 KB, %PDF- header) is found. arXiv DOIs (10.48550/arXiv.2401.01234, version suffixes, old-style hep-th/9901001, or a bare arXiv: id) resolve directly to the arXiv PDF first.

Quick Start

bash
# Prepare a DOI list (one per line)
cat > dois.txt << 'EOF'
10.1007/s00330-010-1783-x
10.1002/mp.12524
10.1148/radiol.13131265
EOF

# Run
python fetch_oa.py dois.txt --output pdfs/ --email your@email.com

# Verbose mode for debugging
python fetch_oa.py dois.txt -o pdfs/ -e your@email.com --verbose

Input Formats

Plain text — one DOI per line:

10.1007/s00330-010-1783-x
10.1002/mp.12524

TSV / CSV with header — must contain a DOI column; optional PMID and Title columns:

tsv
ID	Title	DOI	PMID	Year
1	Some paper	10.1007/s00330-010-1783-x	20628747	2010

Markdown table — a pipe table with a DOI column also works:

markdown
| DOI | PMID | Title |
|-----|------|-------|
| 10.1007/s00330-010-1783-x | 20628747 | Some paper |

When a PMID is available, the PMC lookup is more reliable (PMID → PMCID conversion). When a Title column is present, downloaded PDFs get a best-effort title cross-check (see Retrieval report below).

PMC Download (JS-Challenge Resistant)

PMC web pages may block automated downloads with JavaScript proof-of-work challenges. This tool uses three fallback methods:

Method A: Europe PMC REST API (most reliable)

bash
PMCID="PMC9733600"
curl -sLo output.pdf \
  "https://europepmc.org/backend/ptpmcrender.fcgi?accid=${PMCID}&blobtype=pdf"

Method B: PMC OA FTP Service

bash
curl -s "https://www.ncbi.nlm.nih.gov/pmc/utils/oa/oa.fcgi?id=${PMCID}" | \
    grep -oE 'href="[^"]*\.pdf"' | head -1 | \
    sed 's/href="//;s/"//' | xargs curl -sLo output.pdf

DOI/PMID → PMCID Conversion

bash
# Works with both DOI and PMID
curl -s "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=${DOI}&format=json" | \
    python3 -c "import sys,json; print(json.load(sys.stdin)['records'][0].get('pmcid',''))"

Output

• PDFs saved as {DOI_safe}.pdf (slashes replaced with underscores)
• pdfs/retrieval_report.json — structured per-DOI report (see below)
• manual_needed.txt — DOIs that could not be retrieved via OA
• Summary with arXiv/OA/PMC/fail/skip counts

Retrieval report (--report)

Every run writes a structured report (default <output>/retrieval_report.json, override with --report PATH):

json
{
  "schema_version": 1,
  "generated_by": "fetch_oa.py",
  "counts": {"total": 10, "retrieved": 6, "not_retrieved": 4, "title_mismatch": 1},
  "items": [
    {"doi": "10.1007/...", "pmid": "20628747", "title": "...",
     "status": "oa", "source": "unpaywall", "file": "10.1007_....pdf",
     "size_bytes": 482113, "title_match": "match"}
  ]
}

• status ∈ arxiv | oa | pmc | skip | fail; source names the resolver that succeeded.
• title_match ∈ match | mismatch | unavailable (tri-state). It is best-effort:

it needs a Title column and pdftotext (poppler). When either is missing it is unavailable; a mismatch is flagged for review and never auto-rejects a PDF (guards against a publisher serving a wrong/redirect PDF that still passes the %PDF- check).

Attach PDFs into Zotero ("Find Available PDF")

OA-only resolvers miss paywalled-but-licensed papers. To attach full text inside Zotero at a much higher yield, use references/find_available_pdf.js — a user-run snippet for Zotero's Tools → Developer → Run JavaScript. It triggers Zotero's own addAvailablePDF / addAvailablePDFs and therefore reuses your OpenURL resolver / institutional proxy config; no credentials, proxy hosts, or institutional identifiers are hard-coded or leave your Zotero client. The no-code equivalent is right-click → "Find Available PDF".

This path is user-initiated and depends on your live Zotero session, so its results are recorded manually (not reproducible CI evidence). /lit-sync Phase 2.7 orchestrates both routes (disk OA via this script + in-library via the snippet) and reconciles them in a report.

Requirements

• Python 3.10+ (stdlib only, no pip dependencies)
• Contact email (required by Unpaywall Terms of Service)

API Policies

| Source | Rate Limit | Notes | |--------|-----------|-------| | Unpaywall | 100 req/sec | Email required | | NCBI PMC | 3 req/sec without API key | Add &api_key= for higher limits | | OpenAlex | 100k req/day | Polite pool with email in User-Agent | | Crossref | 50 req/sec with email | Plus service with mailto: in UA | | Europe PMC | No documented limit | Be polite, ≤1 req/sec recommended |

The script uses 0.3–0.5 second delays between requests.

PDF → Markdown Conversion (Optional)

After downloading PDFs, convert them to LLM-friendly Markdown for token-efficient repeated analysis. Uses pymupdf4llm — optimized for academic papers with two-column layout handling and table preservation.

Quick Start

bash
# Install (one-time)
pip install pymupdf4llm

# Convert all PDFs in a directory
python pdf_to_md.py pdfs/

# Convert with verbose output
python pdf_to_md.py pdfs/ -v

# Custom output directory
python pdf_to_md.py pdfs/ -o markdown/

# First 10 pages only (useful for long supplements)
python pdf_to_md.py pdfs/ --pages 0-9

# Overwrite existing conversions
python pdf_to_md.py pdfs/ --force

Combined Workflow

bash
# Step 1: Download PDFs
python fetch_oa.py dois.txt -o pdfs/ -e your@email.com

# Step 2: Convert to Markdown (only successful downloads)
python pdf_to_md.py pdfs/ -v

After conversion, .md files sit alongside .pdf files. Claude Code can then use Read for full content or Grep for targeted extraction — significantly more token-efficient than re-reading PDFs.

When to Convert

| Scenario | Recommendation | |----------|---------------| | Screening/triage (read once) | Skip — read PDF directly | | Data extraction from k≥5 studies | Convert — repeated reads save tokens | | Meta-analysis full pipeline | Convert — papers referenced across multiple phases | | Single paper deep review | Optional — marginal benefit |

Academic Paper Defaults

• Images: Skipped (saves tokens; figures referenced by caption text)
• Tables: lines_strict strategy (preserves grid-line tables accurately)
• Layout: Two-column academic layout handled automatically
• Headers/footers: Removed by pymupdf4llm

Dependency Note

pdf_to_md.py requires pymupdf4llm (AGPL-3.0). This is an optional dependency — fetch_oa.py remains stdlib-only with zero external dependencies. The AGPL license applies to pymupdf4llm itself, not to this skill.

Limitations

• Only retrieves open-access articles. Paywalled articles require institutional access.
• Landing page scraping may fail on publisher-specific JavaScript-heavy pages.
• Some recent articles may not yet be indexed by OA sources.
• PDF→Markdown quality depends on the PDF's text layer. Scanned-only PDFs may produce poor output.

Anti-Hallucination

• Never fabricate file paths, URLs, DOIs, or package names. Verify existence before recommending.
• Never invent journal metadata, impact factors, or submission policies without verification at the journal's website.
• If a tool, package, or resource does not exist or you are unsure, say so explicitly rather than guessing.