Fulltext Retrieval skill

Fulltext Retrieval Skill

Batch download open-access full-text PDFs from a DOI list using legitimate OA APIs only. Paywalled articles fail by design and are listed in manual_needed.txt for institutional access or ILL; never work around a paywall or publisher access control.

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.

Run

Requires Python 3.10+ (stdlib only) and a contact email, which Unpaywall's Terms of Service require. The script paces its requests (0.3–0.5 s delays) for the APIs' rate limits.

python "${CLAUDE_SKILL_DIR}/fetch_oa.py" dois.txt --output pdfs/ --email your@email.com

# Verbose mode for debugging (per-DOI source trace)
python "${CLAUDE_SKILL_DIR}/fetch_oa.py" dois.txt -o pdfs/ -e your@email.com --verbose

Input formats:

• Plain text — one DOI per line.
• TSV / CSV with header, or a Markdown pipe table — must contain a DOI column; optional PMID, Title, and FirstAuthor (surname or full name) columns.

A PMID makes the PMC lookup 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.

Output

• PDFs saved as {DOI_safe}.pdf (slashes replaced with underscores).
• pdfs/retrieval_report.json — structured per-DOI report (below); override with --report PATH.
• <output>/manual_needed.txt — DOIs that could not be retrieved via OA; when a PMCID was resolved, the line also carries it and the PubMed Central article URL to open in a browser.
• Summary with arXiv/OA/PMC/fail/skip counts.

Retrieval report (--report)

Every run writes the report (default <output>/retrieval_report.json), schema 2:

{
  "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, "page_count": 9,
     "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"}}
  ]
}

status (arxiv | oa | pmc | skip | fail), source, and counts.retrieved describe the resolver result, including existing files (skip). They do not count identity-verified papers. No PDF is automatically deleted or rejected. page_count comes from Poppler's pdfinfo (null without it) and is recorded, not judged — a 3-page "article" or a 4-page "book" is worth opening.

source_identity.status	Meaning / action
consistent	Complete normalized title and a compatible DOI/arXiv identifier occur in the bounded first-page front matter, with no supplement / preface / table-of-contents heading and no retraction / erratum / correction / corrigendum / expression-of-concern heading there; 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, a matching title with another DOI/version, or a supplement / preface / table-of-contents file that names the work without being it (supplement_or_front_matter). A retraction notice, erratum, correction, corrigendum or expression of concern whose heading line sits in that area is likewise unresolved (correction_or_retraction_notice). 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.

Evidence is limited to the first page before a recognized abstract/body/reference heading (at most 40 lines / 4,000 characters), so a title cited in the body or references does not count. A title_match of match needs the complete normalized title on up to six consecutive lines; partial overlap is unavailable and low overlap an advisory mismatch. Cover sheets, unusual reading order, short or changed titles and DOI footers outside that area can stay unresolved. PDF metadata and the filename alone are not identity evidence. Explicit arXiv versions must agree; a preprint/published-version DOI difference needs review, not 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")

For paywalled-but-licensed papers the OA resolvers miss, references/find_available_pdf.js is a user-run snippet for Zotero's Tools → Developer → Run JavaScript (no-code equivalent: right-click → "Find Available PDF"). It triggers Zotero's own addAvailablePDF / addAvailablePDFs, so it reuses the user's OpenURL resolver / institutional proxy config; no credentials, proxy hosts, or institutional identifiers are hard-coded or leave the Zotero client. It is user-initiated and depends on the live Zotero session, so record its results manually — they are not reproducible CI evidence. /lit-sync Phase 2.7 orchestrates both routes and reconciles them in a report.

PDF → Markdown Conversion (Optional)

Convert downloaded PDFs to Markdown when the same papers will be read repeatedly (data extraction from k≥5 studies, a meta-analysis pipeline); for one-pass screening, read the PDF directly.

# Install (one-time)
pip install pymupdf4llm

# Convert all PDFs in a directory (.md files land alongside the .pdf files)
python "${CLAUDE_SKILL_DIR}/pdf_to_md.py" pdfs/ -v

# Custom output directory
python "${CLAUDE_SKILL_DIR}/pdf_to_md.py" pdfs/ -o markdown/

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

# Overwrite existing conversions
python "${CLAUDE_SKILL_DIR}/pdf_to_md.py" pdfs/ --force

Images are skipped, so figures survive only as caption text. Scanned-only PDFs (no text layer) convert poorly. pdf_to_md.py needs pymupdf4llm (AGPL-3.0), an optional dependency; fetch_oa.py stays stdlib-only.