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 Extelligence-ai · MCP Server · ★ 397
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is bagel safe to install? View the security audit →
Unlike cat videos, most data are trapped in the physical world. Bagel unlocks them and lets you chat with your physical data—just like you do with ChatGPT. For example: Is my IMU sensor overheating? Can’t wait to try it out? 👉 Check out the Quickstart. 🥯 Key Features Ask in plain language: No deep domain expertise needed. Transparent calculations: Deter
| Stars | 397 |
| Forks | 30 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.0499353629761/100 |
| Open Issues | 26 |
| Last Updated | 2026-09-19 |
| Created | 2025-06-27 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~15k |
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bagel is Query robotics, drone, and IoT data in plain English through an MCP server, with an intelligent edge data reduction pipeline that keeps only the data that matters.. It is categorized as a MCP Server with 397 GitHub stars.
bagel is primarily written in Python. It covers topics such as ardupilot, betaflight, claude.
You can find installation instructions and usage details in the bagel GitHub repository at github.com/Extelligence-ai/bagel. The project has 397 stars and 30 forks, indicating an active community.
bagel is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to bagel on Agent Skills Hub include shodh-memory, quackd, mcp-memory-service. 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: