gpt-all-star — security grade SAFE, quality 69/100

Security audit verdict: SAFE · quality 69/100

Flagged: reads sensitive env vars. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

About gpt-all-star

AI-powered code generation tool for scratch development of web applications with a team collaboration of autonomous AI agents. This is a research-project, and its primary value is to explore the possibility of autonomous AI agents. Table of contents 🏛 Concept 🐳 Getting Started For User For Developer 🕴 Current Situation 🧑‍💻️ UI Project 🔎 Examples ⏱️ Pomodoro Timer 🍻 Contribution 🏛 Concept Build Team, Build App: Simply organize your team and decide on what to build. AI Agent Collaboration: Assemble a group of AI agents and work together to carry out the steps. Choose the right ｌeader for each step. Leaders create a plan of action for each step. Work with team members to complete every task in the action plan. ![gpt-all-star-concept](https://github.com/ky

Quick Facts

Compatible Skills

These tools work well together with gpt-all-star for enhanced workflows:

• py-gpt — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
• LiteWebAgent — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
• LiteMultiAgent — semantic(0.15)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
• openclaw-qa — semantic(0.15)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)

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Frequently Asked Questions

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:

• SlowMist agent-security taxonomy
• Liu et al., An Empirical Study of Agent Skill Security (2026)
• Our census dataset (DOI 10.5281/zenodo.21292799) · Hugging Face mirror
• Full methodology & who runs this