CLIProxyAPI: Wrap Antigravity, ChatGPT Codex, Claude Code, Grok Build, Muse Code, Devin as an OpenAI/Gemini/Claude/Codex

by router-for-me · Codex Skill · ★ 57.9k

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Security audit verdict: SAFE · quality 63/100

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.

antigravityclaude-codecluadecodexdevingeminimuseopenai

Quick Facts

Stars57,858
Forks8,873
LanguageGo
CategoryCodex Skill
LicenseMIT
Quality Score63/100
Open Issues830
Last Updated2026-11-07
Created2025-07-01
Platformsclaude-code, codex, gemini, go
Est. Tokens~17k

About CLIProxyAPI

CLI Proxy API English | 中文 A proxy server that provides OpenAI/Gemini/Claude/Codex compatible API interfaces for CLI. It now also supports OpenAI Codex (GPT models) and Claude Code via OAuth. So you can use local or multi-account CLI access with OpenAI(include Responses)/Gemini/Claude-compatible clients and SDKs. Sponsor This project is sponsored by Z.ai, supporting us with their GLM CODING PLAN.

Compatible Skills

These tools work well together with CLIProxyAPI for enhanced workflows:

  • sub2api — semantic(0.42)+shared_fw(openai)+same_lang+similar_pop+shared_platform (53%)
  • antigravity-claude-proxy — semantic(0.31)+complementary+rare_topics+similar_pop+shared_platform (50%)
  • pal-mcp-server — semantic(0.19)+complementary+shared_fw(openai)+similar_pop+shared_platform (50%)

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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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