License: MIT GitHub stars GitHub forks Latest Release Last Commit

SkillsMP ClawHub Claude Code Plugin Agent Skills Discord

English · 中文 · 📖 Online Docs

Resolve a DOI (or title) to a PDF via a 7-source fallback chain — Unpaywall → Semantic Scholar → arXiv → PubMed Central → bioRxiv/medRxiv → publisher direct → Sci-Hub mirrors. Pure Python stdlib, agent-native CLI with stable JSON envelopes.

Resolve a DOI (or title) to a PDF

• 7-source fallback chain: Unpaywall → Semantic Scholar → arXiv → PubMed Central → bioRxiv/medRxiv → publisher direct (institutional opt-in) → Sci-Hub mirrors (last resort, on by default)
• Title-only input via --title — Crossref + Semantic Scholar resolution with confidence flags
• Auto-named output: {first_author}_{year}_{journal_abbrev}_{short_title}.pdf

Batch + agent-friendly

• --batch dois.txt or --batch - (stdin) for bulk download
• --idempotency-key replays the exact envelope on retry without network I/O
• --stream emits one NDJSON result per line as each DOI resolves
• Skips already-downloaded files unless --overwrite

Built-in correctness

• Stable JSON envelope on stdout, NDJSON progress on stderr, machine-readable schema subcommand
• TTY-aware format default, typed exit codes (0/1/3/4) for orchestrator routing
• SSRF defense + %PDF magic-byte check + 50 MB size cap on every fetch
• Zero runtime dependencies — pure Python stdlib

Cloudflare-blocked PDFs (opt-in)

• PAPER_FETCH_CLOAK=1 retries any 403/429-blocked or JS-challenged PDF URL through CloakBrowser, a stealth Chromium that passes the challenge (approach borrowed from cloakFetch)
• Sits at the download layer, so it applies to every source; off by default, fails closed, operator-controlled
• Returned bytes re-validated through the same %PDF + size checks; result carries via: "cloak"

Works with Claude Code, Codex, Hermes, OpenClaw, ClawHub, pi-mono, and SkillsMP — any agent that supports the Agent Skills format.

The skill is discipline-agnostic — it works for any field, not just life sciences or CS.

In practice, Unpaywall + Semantic Scholar alone cover OA papers in chemistry, materials, economics, psychology, humanities, and every other field via institutional repositories, SSRN, RePEc, and publisher-hosted OA copies.

• python3 (3.8+, stdlib only — no pip install needed)

• (Recommended) An Unpaywall contact email:

  export UNPAYWALL_EMAIL=you@example.com

Without it, Unpaywall is skipped and the remaining 6 sources still work.

# Any agent (Claude Code, Cursor, Copilot, etc.)
npx skills add Agents365-ai/365-skills -g
# Claude Code only
> /plugin marketplace add Agents365-ai/365-skills
> /plugin install paper-fetch

Also published on SkillsMP and ClawHub — each handles updates through its own marketplace.

Just describe what you want:

> Download the AlphaFold2 paper PDF to ~/papers
> Fetch DOI 10.1038/s41586-020-2649-2
> Batch-download every DOI from dois.txt
> Find a PDF for "Attention Is All You Need" and save it
> Preview the resolved PDF URL for 10.1126/science.abj8754 without downloading

Or call the script directly:

# Single DOI
python skills/paper-fetch/scripts/fetch.py 10.1038/s41586-021-03819-2
# By title (resolved to DOI via Crossref + S2 fallback)
python skills/paper-fetch/scripts/fetch.py --title "Highly accurate protein structure prediction with AlphaFold"
# Dry-run preview (no download)
python skills/paper-fetch/scripts/fetch.py 10.1038/s41586-020-2649-2 --dry-run
# Batch with idempotency
python skills/paper-fetch/scripts/fetch.py --batch dois.txt --out ~/papers \
 --idempotency-key monday-review-batch
# Pipe DOIs from another tool
echo 10.1038/s41586-021-03819-2 | python skills/paper-fetch/scripts/fetch.py --batch -
# Agent discovery
python skills/paper-fetch/scripts/fetch.py schema --pretty

Full flag reference and JSON envelope schema in skills/paper-fetch/SKILL.md.

If your institution has a subscription, set PAPER_FETCH_INSTITUTIONAL=1 to enable the publisher-direct fallback. Your IP / cookies / EZproxy authorize the fetch; the skill adds a 1 req/s rate limiter to keep batch jobs within publisher ToS.

export PAPER_FETCH_INSTITUTIONAL=1

See plan/institutional-access.md for design details.

Some publishers (e.g. science.org) sit behind Cloudflare, which serves a 403/429 or a "Just a moment..." JS challenge to plain HTTP clients instead of the PDF. Set PAPER_FETCH_CLOAK=1 to retry those URLs through CloakBrowser — a stealth Chromium that passes the challenge. The approach is borrowed from cloakFetch.

# Requires a Python with `cloakbrowser` importable (pip install cloakbrowser)
export PAPER_FETCH_CLOAK=1
export CLOAKBROWSER_PYTHON="$HOME/github/CloakBrowser/.venv/bin/python" # if not auto-detected
export PAPER_FETCH_CLOAK_HEADED=1 # for hard challenges (e.g. science.org) that defeat headless

The fallback lives at the download layer (so it covers every source), re-validates returned bytes through the same %PDF + 50 MB checks, fails closed when CloakBrowser is unavailable, and is operator-controlled — the agent cannot enable it. Successful cloak downloads carry via: "cloak" in the result.

By default the browser runs headless; harder challenges (e.g. science.org) get stuck on "Just a moment..." in headless mode, so set PAPER_FETCH_CLOAK_HEADED=1 for a visible window that clears them (needs a display). The in-page fetch is same-origin, so it works for direct PDF links on the blocked host (e.g. www.science.org/doi/pdf/...); cross-origin-redirecting URLs fall through. See skills/paper-fetch/SKILL.md (CloakBrowser access) for details.

• Some publisher redirects return an HTML landing page; the %PDF header check rejects them
• No browser automation — no CAPTCHA solving, no Playwright, no stealth
• SSRF defense rejects private IPs, non-http(s) schemes, non-80/443 ports, cloud metadata hosts
• 50 MB cap per PDF download

Part of the Agents365-ai research-skill family — pick the right tool for the job:

• Discord: https://discord.gg/79JF5Atuk
• WeChat: scan the QR code below

WeChat Community Group

If this skill helps you, consider supporting the author:

Agents365-ai

• GitHub: https://github.com/Agents365-ai
• Bilibili: https://space.bilibili.com/441831884

MIT