gemini-for-claude-code: This python program allows you to use Claude Code with Google's Gemini models

by coffeegrind123 · Claude Skill · ★ 354

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

Security audit verdict: SAFE · quality 75/100

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.

anthropicclaudeclaude-codegeminigooglelitellm

Quick Facts

Stars354
Forks64
LanguagePython
CategoryClaude Skill
LicenseWTFPL
Quality Score75/100
Open Issues9
Last Updated2026-02-19
Created2025-05-18
Platformsclaude-code, gemini, python
Est. Tokens~14k

About gemini-for-claude-code

FOR OFFICIAL GOOGLE AI CLI TOOL SEE https://github.com/google-gemini/gemini-cli Gemini for Claude Code: An Anthropic-Compatible Proxy This server acts as a bridge, enabling you to use Claude Code with Google's powerful Gemini models. It translates API requests and responses between the Anthropic format (used by Claude Code) and the Gemini format (via LiteLLM), allowing seamless integration.

Compatible Skills

These tools work well together with gemini-for-claude-code for enhanced workflows:

  • claude-code-gpt-5-codex — semantic(0.23)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • google_workspace_mcp — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)
  • mcp-google-sheets — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • claude-skills — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)
  • AssemblyZero — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)

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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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