scoping-a-custom-modellisted

Decide whether a task actually needs a custom trained model, or whether a better prompt, retrieval, a smaller hosted model, or routing between models would do the job, and if a custom model is right, work out what it has to be good at and how you will know. Use when someone says they want to fine-tune, train their own model, run AI locally, offline, or with no network connection, cut their AI bill, own their model, get a model to answer the same way every time instead of drifting, wonders if an API alone is enough, or wants a model to behave a specific way, and it is not yet clear that training is the answer. Also the entry point to the other open model skills. Not for choosing a specific base model once the decision to train is made, not for building a dataset, and not for any step after training.
ErtasAI/open-model-skills · ★ 2 · AI & Automation · score 76

Install: claude install-skill ErtasAI/open-model-skills

# Scoping a custom model Start here when it is not yet clear whether the answer to a problem is a custom trained model. Most of the time someone arrives already convinced they want to fine-tune, run something offline, or cut a bill, without having named the actual constraint behind that request. This skill exists to slow that down by one step: name the constraint, rule out the cheaper fixes, and only then define what training would have to deliver. ## Name the constraint first People rarely open with a constraint. They open with a solution: "I want to fine-tune a model," "I want to run this offline," "I want my own model." Each of those is a proxy for one of five real constraints. Work out which one it actually is before going further, because the rest of this skill, and which of the other eight skills to go to next, depends on the answer. | What someone says | What it usually means | |---|---| | "It costs too much" | Cost | | "It's too slow" or "the round trip kills it" | Latency | | "The data cannot leave" | Privacy | | "It has to work with no connection" | Offline operation | | "It keeps getting this wrong" or "it's inconsistent" | Behaviour consistency | If none of these fit, ask directly. A bare "I want my own model" with no constraint underneath it is usually enthusiasm rather than a requirement, and training against enthusiasm produces a model nobody actually asked for. ## Four things to try before agreeing to train Training is the slowest, most expensive way to