qwen_cli_coder: Community fork of Google's Gemini CLI for Qwen AI models

by dinoanderson · Agent Tool · ★ 60

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

Security audit verdict: SAFE · quality 67/100

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.

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.

aialibabacloudautomationchineseclicoding-agentscoding-assistantcommand-line-tooldeveloper-toolsllm-agents

Quick Facts

Stars60
Forks24
LanguageTypeScript
CategoryAgent Tool
LicenseApache-2.0
Quality Score67/100
Last Updated2025-07-05
Created2025-06-29
Platformsbrowser, cli, gemini, node
Est. Tokens~215k

About qwen_cli_coder

Qwen CLI - Community Fork English | 简体中文 A community fork of Google's Gemini CLI, modified to work with Qwen models from Alibaba Cloud This repository contains a community-maintained fork of Google's Gemini CLI, modified to work with Qwen models.

Compatible Skills

These tools work well together with qwen_cli_coder for enhanced workflows:

  • sage — semantic(0.17)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • gemini-cli-desktop — semantic(0.27)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • rover — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • claude-code-open — semantic(0.16)+complementary+rare_topics+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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