ai-model-evaluation

What problem does it solve?

This Skill provides a repeatable, structured framework for product managers to evaluate and compare LLMs, ML APIs, and fine-tuned models so teams can select the best model or vendor while balancing quality, latency, cost, compliance, and vendor risk.

Core Features & Use Cases

• Structured evaluation matrix: Step-by-step guidance to score candidates across quality, latency, cost, context window, fine-tuning support, compliance, and vendor lock-in.
• Operational and cost analysis: Latency and throughput checks, context window sizing, cost-per-token modelling at scale, and recommendations for caching, batching, or RAG alternatives.
• Decision support and reporting: Generates a scored comparison, top recommendation, risks & mitigations, and a suggested proof-of-concept scope for build vs API vs fine-tune decisions.
• Use Case: Ideal when choosing between foundation model APIs (OpenAI, Anthropic, Google), open-weight models (Llama, Mistral), or fine-tuned alternatives for tasks like summarization, classification, code generation, or RAG.

Quick Start

Use the ai-model-evaluation skill to evaluate three candidate models for a customer support summarization feature given expected latency, monthly volume, and privacy requirements.