ref-downloader: Batch-download reference PDFs from a DOI or paper PDF using Crossref and your institutional Edge session

by ltczding-gif · Codex Skill · ★ 139

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install ref-downloader

git clone https://github.com/ltczding-gif/ref-downloader.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 72/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

academic-toolsagent-skillsclaude-codecodex-skillscrossrefliterature-reviewpdf-downloaderplaywrightpythonzotero

Quick Facts

Stars139
Forks5
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score72/100
Last Updated2026-06-01
Created2026-05-11
Platformsclaude-code, codex, python
Est. Tokens~36k

About ref-downloader

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.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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