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 KuvopLLC · MCP Server · ★ 66
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is better-bear safe to install? View the security audit →
better bear MCP server and CLI for Bear notes via CloudKit. Includes a context library — a curated, synced folder of notes optimized for LLM consumption, inspired by Karpathy's LLM Knowledge Base pattern. Full docs: better-bear.com Install Install the CLI, then connect to Claude: curl -sL https://raw.githubusercontent.com/KuvopLLC/better-bear/main/install.sh | bash bcli au
| Stars | 66 |
| Forks | 8 |
| Language | Swift |
| Category | MCP Server |
| License | MIT |
| Quality Score | 60.1218029103038/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-23 |
| Created | 2026-02-16 |
| Platforms | claude-code, cli, mcp |
| Est. Tokens | ~66k |
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better-bear is an open-source mcp server by KuvopLLC with 66 GitHub stars.
better-bear is primarily written in Swift. It covers topics such as ai, bear-app, bear-notes.
You can find installation instructions and usage details in the better-bear GitHub repository at github.com/KuvopLLC/better-bear. The project has 66 stars and 8 forks, indicating an active community.
better-bear is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to better-bear on Agent Skills Hub include mcp-server-atlassian-bitbucket, toolhive-studio, apple-notes-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: