Scenario Model Training

Overview

Train a custom model when one look must hold across many assets: an icon set, a recurring character, a product line. Prompts, references, and control maps are cheaper first steps: see scenario-consistency.

The judgment calls live in two references: references/base-model-selection.md (the user interview that feeds recommend_training) and references/dataset-curation.md (dataset size, image rules, captions, and review per training type).

Connection and the core generation loop: see the scenario skill. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Training tools are not in the default toolset: get schemas with scenario_tools_search, run reads (recommend_training, model_get) via scenario_tool_execute_read and writes (model_create, train, model_update) via scenario_tool_execute_write, or reconnect with ?toolsets=full.

Quick reference