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 Agentic-Systems-Lab · Agent Tool · ★ 251
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is rigorous safe to install? View the security audit →
Rigorous AI-Powered Scientific Manuscript Analysis v0.2 Now Live: The latest version of the AI Reviewer (v0.2) is now available at https://www.rigorous.review/. Upload your manuscript, provide context on your target journal and receive structured feedback directly online via an interactive interface — now with progress tracking built in. Once initial testing of v0.2 is complete, we will make all module prompts open source to promote transparency and enable community contributions. Help Us Improve! Please provide feedback via this short feedback form to help us improve the system. Support AI Reviewer v0.3 by rocking some peer-reviewed merch 👕🧠 – grab yours here – GitHub contributors get free gear. Vision This repository is intended for tools that make the creation, evaluation, and distribution of scientific knowledge more transparent, cheaper, faster, and better. Let's build this future together! Project Structure Agent1PeerReview: Multiagent AI review system for comprehensive manuscript analysis, detailed feedback, and PDF report generation (v0.1). Agent2OutletFit: (In Development) Tool for evaluating manuscript fit with target journals/conferences.
| Stars | 251 |
| Forks | 20 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62.9783779663021/100 |
| Open Issues | 3 |
| Last Updated | 2025-08-08 |
| Created | 2025-04-09 |
| Platforms | python |
| Est. Tokens | ~12k |
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rigorous is A comprehensive suite of tools, built to liberate science by making the creation, evaluation, and dissemination of research more transparent, affordable, and efficient.. It is categorized as a Agent Tool with 251 GitHub stars.
rigorous is primarily written in Python. It covers topics such as academic-paper, academic-writing, ai-agents.
You can find installation instructions and usage details in the rigorous GitHub repository at github.com/Agentic-Systems-Lab/rigorous. The project has 251 stars and 20 forks, indicating an active community.
rigorous is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to rigorous on Agent Skills Hub include web-agent-protocol, cheatengine-mcp-bridge, ScienceClaw. 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: