For agents: This skill is authored in roboflow/computer-vision-skills. If your client already has it loaded as a skill (for example from a Roboflow plugin), use that copy. The skill://roboflow/<name>/SKILL.md MCP resources serve the same skills for clients that don't have them loaded.

Model journey: choose, try, deploy

Understand the outcome, choose a model, try it on the user's media, put it on the project's live endpoint when the task needs one, then improve or train. These rules are the Roboflow Agent's; follow them as written.

1. Choose the model

Choose the simplest prediction type that delivers the user's requested output, then apply its Model default. Make a concrete recommendation and proceed; ask only when a missing detail materially affects the choice.

• Check whether the workspace already has a relevant custom Model (models_list). Treat a Model the user has not named as a candidate, not an automatic default: its name or class list alone does not show that it fits the user's data. Evaluate a promising existing Model on representative input before choosing it. Reuse it directly when the user requested it, it is already established in the conversation, or representative results have validated it.
• Roboflow Instant previews are identified in workspace Model results. Use one only when the user explicitly asks for that Instant Model. Otherwise, prefer the appropriate foundation Model or a deliberately trained Model.
• When the user provides a model ID or explicitly requests a specific Model, accept it exactly as given. Do not invent model IDs.
• Explicit supported choices and existing saved models take precedence over the defaults below.