awesome-auditable-ai — not yet audited

by yzhao062 · Agent Tool · ★ 145

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

About awesome-auditable-ai

Auditing AI agents: a curated list of papers, tools, datasets, benchmarks, and standards covering reliability, monitoring, failure attribution, and decision records.

agent-evaluationagent-reliabilityagentic-aiai-agentsai-safetyauditableauditingawesomeawesome-listfailure-attribution

Quick Facts

Stars145
Forks33
LanguagePython
CategoryAgent Tool
LicenseCC0-1.0
Quality Score57.0759426788749/100
Open Issues2
Last Updated2026-09-17
Created2026-06-24
Platformspython
Est. Tokens~13k

Compatible Skills

These tools work well together with awesome-auditable-ai for enhanced workflows:

awesome-auditable-ai alternative? Top 6 similar tools

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Frequently Asked Questions

What is awesome-auditable-ai?

awesome-auditable-ai is Auditing AI agents: a curated list of papers, tools, datasets, benchmarks, and standards covering reliability, monitoring, failure attribution, and decision records.. It is categorized as a Agent Tool with 145 GitHub stars.

What programming language is awesome-auditable-ai written in?

awesome-auditable-ai is primarily written in Python. It covers topics such as agent-evaluation, agent-reliability, agentic-ai.

How do I install or use awesome-auditable-ai?

You can find installation instructions and usage details in the awesome-auditable-ai GitHub repository at github.com/yzhao062/awesome-auditable-ai. The project has 145 stars and 33 forks, indicating an active community.

What license does awesome-auditable-ai use?

awesome-auditable-ai is released under the CC0-1.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to awesome-auditable-ai?

The top alternatives to awesome-auditable-ai on Agent Skills Hub include awesome-loop-engineering, awesome-agent-skills, awesome-ai-coding-techniques. 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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