OpenBitFun: OpenBitFun combines a high-performance agent runtime written in Rust with a polished desktop application

Security audit verdict: SAFE · quality 60/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

About OpenBitFun

English · 中文 Built to keep going. OpenBitFun is an open-source desktop workspace for AI agents. It brings together a fast, reliable Rust Agent Runtime, a powerful, flexible Agent Harness, and a polished desktop experience to make working with agents straightforward. Use AI to write code, fix bugs, research a topic, draft reports, create presentations, and build tools for your daily work.

Compatible Skills

These tools work well together with OpenBitFun for enhanced workflows:

• agent-desktop — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
• pi_agent_rust — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (60%)
• zerostack — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (59%)
• zeptoclaw — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (57%)
• nuphus-mcp — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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
• How we grade: all 42 rules, the source tiers and known limits
• Full methodology & who runs this