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 Hylouis233 · MCP Server · ★ 101
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is bibverify safe to install? View the security audit →
Bibverify Verify bibliographic existence and metadata consistency without destructive edits. Chinese · Install · Quick start · MCP · Development Bibverify is a BibTeX metadata verification tool for researchers, editors, automation, and AI assistants. It starts with exact DOI, PMID, PMCID, or arXiv identifiers and then scores candidates from title, author, year, venue, and pagination evidence. Bibverify evaluates whether a bibliographic record can be located in the queried sources and whether its metadata agrees. It does not prove that research findings are true, data is authentic, or a venue is reputable. A missing database record is not evidence that a referen
| Stars | 101 |
| Forks | 0 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77.7824471640812/100 |
| Last Updated | 2026-09-11 |
| Created | 2025-10-11 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~17k |
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bibverify is Cross-platform BibTeX verification for researchers and AI assistants — CLI, Python API, MCP server, and explainable multi-source matching.. It is categorized as a MCP Server with 101 GitHub stars.
bibverify is primarily written in Python. It covers topics such as bibliography, bibtex, citation-verification.
You can find installation instructions and usage details in the bibverify GitHub repository at github.com/Hylouis233/bibverify. The project has 101 stars and 0 forks, indicating an active community.
bibverify is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to bibverify on Agent Skills Hub include cli-anything-zotero, OneCite, openevidence-mcp. 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: