playbooks — security grade SAFE, quality 59/100

Security audit verdict: SAFE · quality 59/100

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 playbooks-ai · MCP Server · ★ 91

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About playbooks

[](https://github

agent-orchestrationai-agentsai-agents-frameworkai-automationcontext-engineeringconversational-aillmllm-frameworklow-codemcp

Quick Facts

Stars91
Forks15
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score59.0532268985734/100
Open Issues14
Last Updated2026-08-11
Created2024-09-18
Platformsmcp, python
Est. Tokens~19k

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Frequently Asked Questions

What is playbooks?

playbooks is ▶📚 Playbooks is a semantic programming system for AI agents. It is categorized as a MCP Server with 91 GitHub stars.

What programming language is playbooks written in?

playbooks is primarily written in Python. It covers topics such as agent-orchestration, ai-agents, ai-agents-framework.

How do I install or use playbooks?

You can find installation instructions and usage details in the playbooks GitHub repository at github.com/playbooks-ai/playbooks. The project has 91 stars and 15 forks, indicating an active community.

What license does playbooks use?

playbooks is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to playbooks?

The top alternatives to playbooks on Agent Skills Hub include agents, gemini-flow, orchestkit. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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