Decide Fit First

• Core job: Private LLM and vision via LM Studio. Auto-loads models. For text chat, code, image analysis. Default: mistralai/ministral-3-3b…
• Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
• Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

• Structure: The skill is organized around “When to use this skill”, “When not to use this skill”, “Quick Start”, “Features”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
• Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
• Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

• Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
• Invocation: Name lm-studio-private directly; if the source includes slash commands, start with the command and then add task context.
• Validation: Start small and check whether the result follows “When to use this skill / When not to use this skill / Quick Start” before expanding.

Boundaries And Review

• Dependencies: It usually needs no extra API key, so start with a small validation task.
• Permissions: Declared permissions include read; ask the agent to state file, command, and rollback boundaries before acting.
• Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.