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 sipyourdrink-ltd · MCP Server · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is bernstein safe to install? View the security audit →
"To achieve great things, two things are needed: a plan and not quite enough time." - Leonard Bernstein why the name? Bernstein is named after Leonard Bernstein, the American conductor and composer. The project orchestrates a crew of CLI coding agents the way Bernstein conducted the New York Philharmonic: every player on cue, the score deterministic, the conductor accountable for the result. He is the original orchestrator the project takes its name from. deterministic multi-agent CLI orchestration [. No model in the coordination loop, so parallel runs in per-task git worktrees replay byte-identically. Sign. It is categorized as a MCP Server with 1.3k GitHub stars.
bernstein is primarily written in Python. It covers topics such as agent-orchestrator, ai-agents, aider.
You can find installation instructions and usage details in the bernstein GitHub repository at github.com/sipyourdrink-ltd/bernstein. The project has 1.3k stars and 171 forks, indicating an active community.
bernstein is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to bernstein on Agent Skills Hub include parallel-code, linkedin-mcp-server, AgentsMesh. 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.
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