fulltext-retrieval
The fulltext-retrieval skill batch downloads open-access PDF files from a list of DOIs by querying multiple legitimate APIs (Unpaywall, PMC, OpenAlex, Crossref) in sequence until valid PDFs are found. Use this skill when you need to retrieve full-text research articles for analysis, particularly for token-efficient processing via optional PDF-to-Markdown conversion for large-scale document analysis workflows.
git clone --depth 1 https://github.com/Aperivue/medsci-skills /tmp/fulltext-retrieval && cp -r /tmp/fulltext-retrieval/skills/fulltext-retrieval ~/.claude/skills/fulltext-retrievalSKILL.md
# 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](https://github.com/pymupdf/RAG) — 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. ClaMedical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles, abstracts, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), manuscripts for high-impact medical AI journals (Lancet Digital Health, Radiology, Radiology-AI, npj Digital Medicine, Nature Medicine), preprints (medRxiv/arXiv), GitHub README + CITATION.cff + Zenodo archives, and Hugging Face model/dataset cards. Integrates TRIPOD+AI, CLAIM 2024, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting requirements with generative engine optimization (GEO) principles. Produces a visible pass/fail checklist.
>
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>
Check manuscript compliance with medical research reporting guidelines. Supports 49 guidelines including STROBE, STROBE-MR, RECORD, REMARK (prognostic tumor-marker studies), TARGET (target trial emulation), GATHER (burden-of-disease / health-estimate modeling), CONSORT, CONSORT-AI, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, PGS-RS, ARRIVE, PRISMA, PRISMA 2020 for Abstracts, PRISMA-DTA, PRISMA-P, PRISMA-ScR (scoping reviews), CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, MI-CLEAR-LLM, SQUIRE 2.0, CLEAR, MOOSE, GRRAS, SWiM, AMSTAR 2, CHEERS 2022, CROSS (survey studies), SRQR and COREQ (qualitative research), and risk of bias tools (QUADAS-3, QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Generates item-by-item assessment with PRESENT/MISSING/PARTIAL status.