Route coding tasks across planning, implementation, and review models with automated test gates.
npx skills add https://github.com/Adam-Luciano-MDB/multi-model-flow --skill mmf
Please help me install this Agent Skill. Skill: mmf Source: https://github.com/Adam-Luciano-MDB/multi-model-flow/tree/main/skills/mmf Command: npx skills add https://github.com/Adam-Luciano-MDB/multi-model-flow --skill mmf
This skill solves the inefficiency of using expensive, high-tier AI models for every coding task by implementing a tiered routing system that balances cost, speed, and quality.
Invoke the multi-model-flow skill by typing /mmf followed by your task description and any desired flags like [auto] or [openrouter].
High-intent search queries and answers about installing and using this skill.
You can optimize LLM API costs for coding workflows by using a tiered routing system that delegates architectural planning to high-reasoning models and routine implementation to cost-effective local or cloud models.
Yes, you can use local Ollama models for AI code generation by integrating them into a multi-phase pipeline that offloads routine implementation and automated testing from expensive cloud LLMs.
Automated model routing in a coding pipeline assigns high-stakes planning to high-tier LLMs, implementation to cheaper models, and reviews to mid-tier models, balancing development speed, cost, and code quality.
Automated test gates block unverified LLM generated code, while tiered recovery loops attempt to resolve bugs using cheaper models before escalating to a full architectural re-plan.
Multi-model routing works for complex software refactoring by ensuring high-stakes architectural decisions are handled by advanced reasoning models while offloading routine implementation to cost-effective endpoints.
You should avoid a tiered multi-model coding pipeline for simple, single-file edits where the overhead of routing between local Ollama instances and cloud LLMs outweighs the cost savings.
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