By ypollak2
Intelligently route AI tasks across 20+ LLM providers (OpenAI, Anthropic, Gemini, Ollama, etc.) using complexity-first model selection to minimize costs while preserving output quality. Automatically classify prompts and dispatch to the cheapest capable model, track cross-session savings, and receive weekly cost digests via Slack/Discord.
Automates the full release pipeline for llm-router. Run this skill whenever
Route a task to the best LLM based on task type and complexity
Route tasks to the cheapest capable model automatically using llm-router MCP tools.
Track and report how much you've saved by routing tasks to cheaper models.
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npx claudepluginhub ypollak2/llm-router --plugin llm-routerA real-time directory of AI models that allows your AI agent to advise and pick the ideal LLM for the user's task.
OpenRouter SDK plugin - unified interface for 500+ LLM models with intelligent routing, cost optimization, and framework integrations (Vercel AI SDK, LangChain, OpenAI SDK, PydanticAI)
Flagship+ skill pack for OpenRouter - 30 skills for multi-model routing, fallbacks, and LLM gateway mastery
Teach Claude to detect, wire, and troubleshoot FreeRide — a local OpenAI-compatible gateway routing across free-tier providers (OpenRouter, Groq, NVIDIA NIM, Cloudflare Workers AI, HuggingFace).
Run any model with an Anthropic- or OpenAI-compatible API (e.g. DeepSeek, GLM, Kimi, Qwen, MiniMax) — even your Codex subscription — as real Claude Code workflows, agent-team teammates, or one-shot subagents, driven exactly like native ones. Your main session's own auth is untouched (OAuth subscription or API key, either works); API-key providers bill the provider key via apiKeyHelper, while a Codex subscription bills through a local OAuth daemon — each worker receives its credential on demand, never through its env or argv. Requires the `cc-fleet` binary on PATH, installed separately.
When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.