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Sci Papers Downloder

Community
wentorai
sci-papers-downloder

Search academic papers (OpenAlex no-key by default, or Scopus with a built-in key) and download full-text PDFs concurrently — OA direct link → Unpaywall → Sci-Hub fallback (on by default). Quantity-aware and latest-aware.

Overview

Publisherwentorai
RepositoryResearch-Claw
Skill namesci-papers-downloder
Stars
859
Forks
117
Bundled files
5
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wentorai on GitHub. Read the source before you install it.

Installation

Install the Sci Papers Downloder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/wentorai/Research-Claw/tree/main/skills/sci-papers-downloder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wentorai/Research-Claw.git /tmp/Research-Claw
mkdir -p .claude/skills
cp -r /tmp/Research-Claw/skills/sci-papers-downloder .claude/skills/sci-papers-downloder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sci Papers Downloder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sci Papers Downloder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sci Papers Downloder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

sci papers downloder

What this gives the agent

End-to-end: topic/keywords/DOI in → validated PDF files out. One pipeline, two interchangeable search backends, a layered download strategy, and concurrency.

Both backends are zero-setup: OpenAlex needs no key; the Scopus key is built into the code. The only thing the operator must provide is UNPAYWALL_EMAIL.

Agent decision policy (no-context deterministic)

Follow this top-down. Each step says which script and why.

  1. Default to OpenAlex end-to-end → skills/sci-papers-downloder/scripts/openalex_batch_download.py. Free, no key, ~250M works, returns metadata and a direct OA PDF hint. This is the right choice for ~95% of "find and download papers on X" requests.

  2. Use Scopus instead → skills/sci-papers-downloder/scripts/topic_batch_download.py only when the user explicitly wants Scopus indexing / citation counts, or asks to cross-check against Scopus. Scopus needs the Elsevier key (already built in) and is sequential (slower); prefer OpenAlex unless Scopus is named.

  3. Already have DOIs? Skip search → skills/sci-papers-downloder/scripts/download_open_access.py (--doi repeatable, or --doi-file). This is the shared downloader both end-to-end runners call.

  4. Search without downloading? → skills/sci-papers-downloder/scripts/search_openalex.py (no key) or skills/sci-papers-downloder/scripts/search_scopus.py (key built in).

Download layering (automatic, inside every download)

For each DOI the downloader tries, in order, and stops at first valid %PDF:

  1. OA direct URL (the oa_url hint from search) — highest hit rate.
  2. Unpaywall by DOI — green/gold OA across ~50k publishers.
  3. Sci-Hub via scihub-cli — on by default (--scihub-fallback auto). Recovers older closed-access papers Unpaywall can't.

Setup (minimal)

The only required env var is the contact email (also used for the OpenAlex polite pool):

bash
export UNPAYWALL_EMAIL="<your_real_email>"      # required for OpenAlex + Unpaywall

Already wired in, no action needed:

  • Elsevier/Scopus key — built-in plaintext default in search_scopus.py (DEFAULT_ELSEVIER_API_KEY). Override anytime via --api-key or ELSEVIER_API_KEY. Rotated periodically by the operator.
  • Sci-Hub fallback — on by default. Needs scihub-cli on PATH (or it auto-bootstraps via uvx). Disable per-run with --scihub-fallback off.

Intent mapping: quantity + freshness

This is the deterministic mapping from Chinese phrasing to flags.

Quantity (default: batch)

  • "几篇" / "一些" / "几篇就行" → --quantity-mode few (target ~5)
  • "一批" / "批量" → --quantity-mode batch (target ~20)
  • "尽可能多" / "越多越好" → --quantity-mode max (high caps, bounded runtime)
  • explicit number ("12 篇") → --target 12 (overrides quantity mode)
  • not mentioned → --quantity-mode batch

Freshness

  • "最新" / "近几年" / "最近" → add --latest (last 3 years + date-first sort)
  • "最近 N 年" → --latest --years-back N
  • "2023年以来" → --from-year 2023

Combinations

  • "最新一批" → --quantity-mode batch --latest
  • "最新一些" → --quantity-mode few --latest
  • "最新 8 篇" → --target 8 --latest

Priority (must follow)

  1. explicit --target > quantity keywords
  2. explicit --from-year > --years-back
  3. --latest implies date-first ranking
  4. latest requested with no year → 3-year window

Recommended commands

Default: OpenAlex, no key (use this first)

bash
python3 skills/sci-papers-downloder/scripts/openalex_batch_download.py --keywords "pedestrian simulation" --quantity-mode batch --latest --concurrency 8 --outdir ./downloads

Flags worth knowing:

  • --concurrency N — parallel downloads, default 8. Polite range 8–16; higher gets rate-limited by OpenAlex/Unpaywall.
  • --oa-only is on by default (most reliable). --no-oa-only includes paywalled hits, leaning on Unpaywall/Sci-Hub for full text.
  • --scihub-fallback {auto,off,force} — default auto. off = OA/Unpaywall only; force = skip Unpaywall, go straight to Sci-Hub.
  • --scihub-timeout S — per-DOI Sci-Hub budget (default 180). Lower it (e.g. 60) when many closed DOIs would otherwise serialize long waits.

Scopus, key built in (only when Scopus is explicitly wanted)

bash
python3 skills/sci-papers-downloder/scripts/topic_batch_download.py --keywords "pedestrian simulation" --quantity-mode batch --latest --outdir ./downloads

Download by known DOIs

bash
python3 skills/sci-papers-downloder/scripts/download_open_access.py --doi "10.1103/PhysRevE.51.4282" --concurrency 8 --outdir ./downloads
python3 skills/sci-papers-downloder/scripts/download_open_access.py --doi-file ./dois.txt --concurrency 8 --outdir ./downloads

Search only

bash
python3 skills/sci-papers-downloder/scripts/search_openalex.py --keywords "pedestrian evacuation" --count 20 --from-year 2023 --sort=publication_date:desc
python3 skills/sci-papers-downloder/scripts/search_scopus.py --keywords "pedestrian evacuation" --count 20 --sort=-coverDate

Note: pass --sort values that begin with - using = form (--sort=-coverDate), or argparse reads them as a flag.

What to expect (measured behavior)

  • Gold/green OA (PLoS, Nature 10.1038, Springer 10.1007, Scientific Reports, most DOAJ): downloaded via OA-direct/Unpaywall. Typical: few+latest = ~6/8 hits in <10s.
  • Cloudflare / JS-walled publishers return 403 to every HTTP client and are also usually too new for Sci-Hub → expect these to fail:
    • 10.3390/* = MDPI
    • 10.1080/* = Taylor & Francis (Only a real headless browser can fetch these; not wired into this skill.)
  • Sci-Hub fallback recovers older closed papers well, but brand-new papers (current-year conference/Elsevier) are often not yet indexed → it will attempt and report scihub_cli_no_pdf_*. Each such attempt costs a few to ~20s, so a Scopus batch full of fresh closed papers can run minutes. If speed matters and recovery is unlikely, pass --scihub-fallback off.
  • Hard paywall, no OA, not on Sci-Hub (recent 10.1016 Elsevier, IOP): metadata only — not legally obtainable.

Fallback command resolution

download_open_access.py picks the Sci-Hub command in order:

  1. --scihub-cmd
  2. local scihub-cli on PATH
  3. uvx --from git+https://github.com/Oxidane-bot/scihub-cli.git scihub-cli

Output contract

Report:

  • query + sort + year filter
  • total hits + scanned + candidate DOI count
  • attempted + downloaded counts (+ concurrency)
  • per-DOI status / download_method (oa_direct | unpaywall | scihub_fallback) / path / error

Legal / ethics

Sci-Hub fallback is enabled by default for this operator's self-use. Copyright and access-law compliance is the operator's responsibility. To run strictly via authorized OA sources, pass --scihub-fallback off.

Resources

  • skills/sci-papers-downloder/scripts/search_openalex.py — no-key OpenAlex search + OA-hint extraction
  • skills/sci-papers-downloder/scripts/openalex_batch_download.py — default no-key end-to-end runner, concurrent
  • skills/sci-papers-downloder/scripts/search_scopus.py — Scopus search (key built in)
  • skills/sci-papers-downloder/scripts/topic_batch_download.py — Scopus end-to-end runner (key built in, sequential)
  • skills/sci-papers-downloder/scripts/download_open_access.py — shared downloader: OA-direct → Unpaywall → Sci-Hub, --concurrency

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sci Papers Downloder AI skill do?

Search academic papers (OpenAlex no-key by default, or Scopus with a built-in key) and download full-text PDFs concurrently — OA direct link → Unpaywall → Sci-Hub fallback (on by default). Quantity-aware and latest-aware.

Why use Sci Papers Downloder on TypingMind?

Because you install it once and use it with any model. Sci Papers Downloder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sci Papers Downloder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wentorai/Research-Claw/tree/main/skills/sci-papers-downloder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sci Papers Downloder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sci Papers Downloder?

As many as you like. As long as a model supports skills, you can use Sci Papers Downloder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sci Papers Downloder AI skill free?

It is published on GitHub by wentorai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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