No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ltczding-gif · Codex Skill · ★ 134
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ref-downloader safe to install? View the security audit →
ref-downloader Stop losing an afternoon to chasing dozens of reference PDFs by hand. One DOI in, every reference PDF out — using your existing institutional access. 中文完整文档 / Full Chinese version Status: beta (v0.3.0). Windows + Microsoft Edge verified path. macOS / Linux / Chromium untested. Expect rough edges around supplementary downloads and publisher-site changes. PR-worthy issues welcome. Heads up — not a paywall bypass. ref-downloader uses your institutional access. If your university or organization subscribes to a journal, those refs work. If they don't, those refs become for you to follow up on by hand. Demo (30-second console preview) text $ python runrefdownloader.py 10.1021/jacs.5c05017 === Ref Downloader Wrapper === DOI: 10.1021/jacs.5c05017 PROJECT: jacs.5c05017 Config: config.example.toml + config.local.toml extractrefs.py Title: Designing Natural Cell-Inspired Heme-Spurred Membrane... References found: 38
| Stars | 134 |
| Forks | 4 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 72.2100904526457/100 |
| Last Updated | 2026-06-01 |
| Created | 2026-05-11 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~36k |
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ref-downloader is Batch-download reference PDFs from a DOI or paper PDF using Crossref and your institutional Edge session.. It is categorized as a Codex Skill with 134 GitHub stars.
ref-downloader is primarily written in Python. It covers topics such as academic-tools, agent-skills, claude-code.
You can find installation instructions and usage details in the ref-downloader GitHub repository at github.com/ltczding-gif/ref-downloader. The project has 134 stars and 4 forks, indicating an active community.
ref-downloader is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ref-downloader on Agent Skills Hub include paper-fetch, nature-academic-search, paper-fetch. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: