opensquilla: Token-Efficient AI Agent with same budget, higher intelligence density

by TokenRhythm · MCP Server · ★ 7.1k

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

How to install opensquilla

git clone https://github.com/TokenRhythm/opensquilla.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 66/100

Flagged: sudo usage. 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.

agentaiai-agentsdeep-learningfoundation-modelsllmmcpmemoryopenclawpython

Quick Facts

Stars7,106
Forks589
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score66/100
Open Issues293
Last Updated2026-11-06
Created2026-05-06
Platformsmcp, python
Est. Tokens~22k

About opensquilla

OpenSquilla — Token-Efficient AI Agent Same budget, more capability, better results. A microkernel AI agent for your CLI, Web UI, and chat channels. English · 中文 · 日本語 · Français · Deutsch · Español News 📢 2026-08-22 — The English version of our technical report is now on aiXiv: [aixiv.2608

Compatible Skills

These tools work well together with opensquilla for enhanced workflows:

  • auto-deep-researcher-24x7 — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • llm_agents — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • SkillClaw — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)

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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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