agents365-ai/365-skills3 files

Paper Fetch

Use whenever the user wants to obtain, download, or fetch a paper's PDF — given a DOI, an arXiv id, a paper title, a citation, or a list of DOIs. Trigger on phrases like "download this paper", "find the PDF for [DOI]", "grab me the [Nature/bioRxiv/arXiv] paper on X", "get the open-access version", "I need this article", or any bulk/batch paper download request, even when the user doesn't explicitly say "PDF" or "DOI". Resolves via Unpaywall → Semantic Scholar → arXiv → PubMed Central → bioRxiv/medRxiv → publisher direct (institutional opt-in) → Sci-Hub mirrors as last-resort fallback.

Specification
Skill ID
agents365-ai/365-skills/paper-fetch
Publisher
agents365-ai
Repository
365-skills
Installs
0
Files
3
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

agents365-ai/365-skills/paper-fetchInstalls these files
  • SKILL.md
  • scripts/cloak_pdf.py
  • scripts/fetch.py

What this skill tells the agent

paper-fetch

Fetch the PDF for a paper given a DOI (or title). Tries multiple sources in priority order and stops at the first hit.

Resolution order

  1. Unpaywall — https://api.unpaywall.org/v2/{doi}?email=$UNPAYWALL_EMAIL, read best_oa_location.url_for_pdf (skipped if UNPAYWALL_EMAIL not set)
  2. Semantic Scholar — https://api.semanticscholar.org/graph/v1/paper/DOI:{doi}?fields=openAccessPdf,externalIds
  3. arXiv — if externalIds.ArXiv present, https://arxiv.org/pdf/{arxiv_id}.pdf
  4. PubMed Central OA — if PMCID present, https://www.ncbi.nlm.nih.gov/pmc/articles/{pmcid}/pdf/
  5. bioRxiv / medRxiv — if DOI prefix is 10.1101, query https://api.biorxiv.org/details/{server}/{doi} for the latest version PDF URL
  6. Publisher direct (institutional mode only — `PAPER_FETCH_INSTITUTIONAL=1`) — DOI-prefix → publisher PDF template (Nature / Science / Wiley / Springer / ACS / PNAS / NEJM / Sage / T&F / Elsevier). The caller's own subscription IP / cookies / EZproxy are what authorize the fetch; unauthorized responses fail the %PDF check and fall through to step 7.
  7. Sci-Hub mirrors (on by default; disable with `PAPER_FETCH_NO_SCIHUB=1`) — last-resort fallback. Tries the mirror list in PAPER_FETCH_SCIHUB_MIRRORS (or built-in defaults sci-hub.ru, sci-hub.st, sci-hub.su, sci-hub.box, sci-hub.red, sci-hub.al, sci-hub.mk, sci-hub.ee) in order; on full miss, scrapes https://www.sci-hub.pub/ once per process for fresh mirrors. CAPTCHA / missing-paper pages have no PDF iframe and fall through silently.
  8. Otherwise → report failure with title/authors so the user can request via ILL

CloakBrowser fallback (download layer, opt-in — `PAPER_FETCH_CLOAK=1`). This is not a separate source: it sits at the download chokepoint, so it applies to any of the sources above. When a resolved PDF URL is blocked by Cloudflare — HTTP 403/429, or a "Just a moment…" HTML interstitial served in place of the file — and the operator opted in, the URL is retried through CloakBrowser (a stealth Chromium that passes the JS challenge) via the cloak_pdf.py companion. Bytes it returns are re-validated through the same %PDF magic-byte + 50 MB checks; on success the result carries via: "cloak". Off by default, fails closed (missing CloakBrowser → silent fall-through), and the agent cannot opt in — see CloakBrowser access below.

If only a title is given, pass it directly via --title "<title>". Resolution chain:

  1. Crossref query.title — primary; covers all major journal/conference DOIs
  2. Semantic Scholar `/paper/search/match` — fallback when Crossref's top match is low-confidence (match_score < 40) or the gap to the runner-up is < 3. Critically, S2 covers arXiv-only preprints (no Crossref DOI). When S2 surfaces a paper that has only an arXiv id, the canonical 10.48550/arXiv.<id> is synthesized so the download chain stays uniform.
  3. Crossref's best guess (low-confidence) — used only when both resolvers struggled. The result envelope sets meta.title_resolution.low_confidence: true plus a low_confidence_reason (score_below_threshold / ambiguous_runner_up) so an agent can either bail or confirm via --dry-run.

Either way the resolved DOI, the winning resolver, the full resolvers_tried list, and the top candidate matches are all surfaced under meta.title_resolution.

If `semanticscholar-skill` is registered, it can serve as a richer pre-step for title → DOI resolution — useful when you also need relevance ranking, snippet search, or citation context, not just a DOI. The agent writes a Python script using the skill's match_title() to read externalIds.DOI, then runs paper-fetch <doi>. When the result has only an ArXiv id (no DOI), synthesize 10.48550/arXiv.<ArXiv> and pass that to paper-fetch.

When only the DOI is needed, --title is the single-command path — paper-fetch's built-in Crossref → S2 chain handles most cases.

Usage

python scripts/fetch.py <DOI> [options]
python scripts/fetch.py --title "<paper title>" [options]
python scripts/fetch.py --batch <FILE|-> [options]
python scripts/fetch.py schema           # machine-readable self-description

Flags

The flags below are the ones an agent composes in normal use. For the complete contract — including --dry-run, --pretty, --stream, --overwrite, --timeout, --version, plus parameter types and exit-code mappings — run python scripts/fetch.py schema (machine-readable, drift-checked via schema_version).

FlagDefaultDescription
doi—DOI to fetch (positional). Use - to read a single DOI from stdin
--title TITLE—Paper title; resolved to a DOI via Crossref before download. Mutually exclusive with positional DOI / --batch
--batch FILE—File with one DOI per line for bulk download. Use - to read from stdin
--out DIRpdfsOutput directory
--formatautojson for agents, text for humans. Auto-detects: json when stdout is not a TTY, text when it is
--idempotency-key KEY—Safe-retry key. Re-running with the same key replays the original envelope from <out>/.paper-fetch-idem/ without network I/O

Agent discovery: schema subcommand

python scripts/fetch.py schema

Emits a complete machine-readable description of the CLI on stdout (no network). Includes cli_version, schema_version, parameter types, exit codes, error codes, envelope shapes, and environment variables. Agents should read this once, cache it against schema_version, and re-read when the cached version drifts.

Output contract

stdout emits a single JSON envelope. Every envelope carries a meta slot.

Success (all DOIs resolved):

{
  "ok": true,
  "data": {
    "results": [
      {
        "doi": "10.1038/s41586-021-03819-2",
        "success": true,
        "source": "unpaywall",
        "pdf_url": "https://www.nature.com/articles/s41586-021-03819-2.pdf",
        "file": "pdfs/Jumper_2021_Highly_accurate_protein_structure_predic.pdf",
        "meta": {"title": "Highly accurate protein structure prediction with AlphaFold", "year": 2021, "author": "Jumper"},
        "sources_tried": ["unpaywall"]
      }
    ],
    "summary": {"total": 1, "succeeded": 1, "failed": 0},
    "next": []
  },
  "meta": {
    "request_id": "req_a908f5156fc1",
    "latency_ms": 2036,
    "schema_version": "1.9.0",
    "cli_version": "0.13.1",
    "sources_tried": ["unpaywall"]
  }
}

Partial (batch mode — some DOIs failed, exit code reflects the failure class):

{
  "ok": "partial",
  "data": {
    "results": [
      { "doi": "10.1038/s41586-021-03819-2", "success": true, "source": "unpaywall", ... },
      {
        "doi": "10.1234/nonexistent",
        "success": false,
        "source": null,
        "pdf_url": null,
        "file": null,
        "meta": {},
        "sources_tried": ["unpaywall", "semantic_scholar"],
        "error": {
          "code": "not_found",
          "message": "No open-access PDF found",
          "retryable": true,
          "retry_after_hours": 168,
          "reason": "OA availability changes over time; retry after embargo lifts or preprint appears"
        }
      }
    ],
    "summary": {"total": 2, "succeeded": 1, "failed": 1},
    "next": ["paper-fetch 10.1234/nonexistent --out pdfs"]
  },
  "meta": { ... }
}

The next slot is an array of suggested follow-up commands: re-invoking them retries only the failed subset. Combine with --idempotency-key to make the whole batch safely retriable without re-downloading the already-succeeded items.

Failure (bad arguments, exit code 3):

{
  "ok": false,
  "error": {
    "code": "validation_error",
    "message": "Provide a DOI or --batch file",
    "retryable": false
  },
  "meta": { ... }
}

Use this skill in a project

Open any RiverX project, search the Skills panel for agents365-ai/365-skills/paper-fetch, and install it in one click.

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