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

# 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, Title, and FirstAuthor columns (first author's surname or full name for corroboration):

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:

| 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). Supply Title where available: a DOI-only worklist can download a PDF but cannot establish title agreement. FirstAuthor is optional additional evidence.

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)

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

Method B: PMC OA FTP Service

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

# 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):

{
  "schema_version": 2,
  "generated_by": "fetch_oa.py",
  "counts": {"total": 4, "retrieved": 3, "not_retrieved": 1, "title_mismatch": 1,
             "source_identity": {"consistent": 1, "conflict": 1, "unresolved": 1, "unavailable": 1}},
  "items": [
    {"doi": "10.1000/synthetic.example", "pmid": "", "title": "Example title",
     "first_author": "", "status": "oa", "source": "unpaywall",
     "file": "10.1000_synthetic.example.pdf", "size_bytes": 482113,
     "file_sha256": "<SHA-256 of the downloaded file>", "title_match": "match",
     "source_identity": {"status": "consistent", "reason": "title_and_identifier_agree",
                         "text_scope": "first_page_front_matter", "title_match": "match",
                         "doi_match": "match", "observed_identifiers": ["10.1000/synthetic.example"],
                         "first_author_match": "unavailable"}}
  ]
}

The example abbreviates items. Legacy status (arxiv | oa | pmc | skip | fail), source, and counts.retrieved retain their resolver-result meaning, including existing files (skip). They do not count identity-verified papers. Report schema 2 adds the file hash and separate identity evidence; no PDF is automatically deleted or rejected.

source_identity.status	Meaning / action
consistent	Complete normalized title and a compatible DOI/arXiv identifier occur in the bounded first-page front matter; an optional supplied author must also match. Evidence agrees, but this is not independent source verification or claim validation.
conflict	Both the title and observed identifier differ. Inspect the PDF and requested record.
unresolved	Evidence is incomplete or ambiguous: title-only, DOI-only, missing author, multiple identifiers, or a matching title with another DOI/version. Inspect before using as evidence.
unavailable	No usable extracted text, Poppler unavailable, no output PDF, or the PDF changed during assessment. No current identity assessment was possible.

title_match keeps its tri-state shape. A match now requires the complete normalized title on up to six consecutive front-matter lines. Case, punctuation and line wrapping are normalized. Scattered matching words cannot establish a match; partial overlap is unavailable, and low overlap is an advisory mismatch.

Evidence is limited to the first page, before a recognized abstract/body/reference heading, at most 40 lines / 4,000 characters. Thus a title cited in the body or references does not establish a title match. These are conservative layout heuristics: cover sheets, unrecognized headings, short or changed titles, unusual reading order and DOI footers outside that area can remain unresolved. PDF metadata and the filename alone are not identity evidence. The CLI compares hashes before extraction and when reporting; changed files cannot inherit the previous text's assessment. Explicit arXiv versions must agree; preprint/published-version DOI differences require review rather than automatic rejection.

Downstream reports must preserve source_identity and file_sha256, keep unresolved items visible, and check the hash still identifies the file being used. Older reports without identity evidence remain unassessed; do not infer identity from retrieved or title_match=match. Full-text conversion does not resolve an identity warning.

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

# 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

# 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.