llmrouterIntelligent LLM proxy that routes requests to appropriate models based on complexity. Save money by using cheaper models for simple tasks. Tested with Anthropic, OpenAI, Gemini, Kimi/Moonshot, and Ollama.
Install via ClawdBot CLI:
clawdbot install alexrudloff/llmrouterGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses sensitive credential files or environment variables
$ANTHROPICCalls external URL not in known-safe list
https://github.com/alexrudloff/llmrouterAI Analysis
The skill acts as a legitimate LLM routing proxy that requires API keys for its stated purpose of model selection. While it accesses sensitive credentials (environment variables) and clones from an external GitHub repository, these actions are consistent with its functionality and documented. No evidence of hidden instructions, credential harvesting beyond declared needs, or obfuscation was found.
Audited Apr 16, 2026 · audit v1.0
Generated Mar 1, 2026
A company uses the LLM router to classify incoming customer queries by complexity, routing simple FAQs to cheaper models like Haiku or GPT-4o-mini, while directing technical issues to more capable models like Claude Opus. This reduces API costs by up to 70% while maintaining response quality for complex cases.
A social media platform employs the router to screen user-generated content, using local Ollama models for basic profanity detection and lightweight classification, then escalating nuanced hate speech or legal concerns to premium models. This balances cost-efficiency with accuracy in high-stakes moderation.
An online learning platform integrates the router to assess student questions, sending basic math problems to free local models and routing advanced physics or coding queries to OpenAI's o3-mini. This enables scalable, personalized tutoring without overspending on simple interactions.
A telehealth app uses the router to classify patient descriptions, with common symptoms handled by fast, low-cost models and complex medical histories forwarded to high-accuracy models for preliminary analysis. It ensures reliable triage while controlling operational expenses in healthcare services.
A fintech firm applies the router to process financial queries, using Gemini Flash for routine data summaries and Claude Sonnet for in-depth risk assessment or regulatory compliance checks. This optimizes model usage across varying analytical depths in finance workflows.
Offer the LLM router as a managed cloud service with tiered pricing based on request volume and model usage, targeting startups and enterprises seeking cost-effective AI routing. Revenue streams include monthly subscriptions and pay-per-request fees, with potential upsells for premium support.
Sell enterprise licenses for self-hosted deployments, providing customization, security compliance, and integration support for large organizations in regulated industries like finance or healthcare. Revenue comes from one-time license fees and annual maintenance contracts, ensuring long-term client relationships.
Provide consulting services to help businesses implement and optimize the router within their existing AI stacks, including configuration tuning, performance monitoring, and workflow automation. Revenue is generated through project-based fees and ongoing retainer agreements for technical support.
💬 Integration Tip
Start with a local classifier using Ollama to minimize costs, then gradually integrate remote providers like Anthropic for higher accuracy in production environments.
Scored Apr 19, 2026
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