mmf

Name: mmf
Availability: InStock
Author: adam-luciano-mdb

Route coding tasks across planning, implementation, and review models with automated test gates.

Updated Jun 17, 2026

Search Tags:#automation #workflow #llm #coding #cost-optimization #ollama

Ready to run

npx skills add https://github.com/Adam-Luciano-MDB/multi-model-flow --skill mmf

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

Tiered Model Routing: Automatically routes planning to Opus, implementation to Haiku or local Ollama models, and reviews to Sonnet.
Automated Recovery: Features a hard test gate and a tiered fix loop that attempts to resolve bugs using cheaper models before escalating to a full re-plan.
Use Case: Use this in a large-scale refactoring project to ensure high-stakes architectural decisions are handled by Opus while routine implementation and testing are offloaded to local or cost-effective cloud models.

Quick Start

Invoke the multi-model-flow skill by typing /mmf followed by your task description and any desired flags like [auto] or [openrouter].

Frequently Asked Questions about mmf

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize LLM API costs for large-scale coding workflows?▼

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.

Can I use local Ollama models for AI code generation and testing?▼

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.

What is automated model routing in a multi-phase coding pipeline?▼

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.

How do automated test gates and recovery loops handle LLM generated code?▼

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.

Does multi-model routing work for complex software refactoring tasks?▼

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.

When should I avoid using a tiered multi-model coding pipeline?▼

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.