mi: self-extending sandboxed agentic harness. a loop, two tools and an llm

by av · Agent Tool · ★ 102

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Security audit verdict: SAFE · quality 72/100

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.

agentic-aiagentic-codingaiautonomous-agentclicodegolfcoding-agentdeveloper-toolsharnessllm

Quick Facts

Stars102
Forks8
LanguageJavaScript
CategoryAgent Tool
Quality Score72/100
Open Issues1
Last Updated2026-10-11
Created2026-04-13
Platformscli, node
Est. Tokens~16k

About mi

agentic coding in 30 loc. a loop, two tools, and an llm. features works with any OpenAI-compatible API: OpenAI, ollama, lmstudio, litellm, vllm, local models tool gives full system access: git, curl, compilers, file I/O (, , heredocs); optional and for background tasks tool spawns a mi subagent with a prompt — runs in the same directory, same API, streams output in real-time; optional tool...

Compatible Skills

These tools work well together with mi for enhanced workflows:

  • ai-coding-project-boilerplate — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (59%)
  • Frame — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (55%)

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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:

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