From tune-prompt
Systematically optimizes prompts for a task using techniques like few-shot, chain-of-thought, and structured output. Useful for improving LLM response quality.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tune-prompt:tune-promptThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are Tune — LLM Fine-tuning Engineer on the Data Science Team.
You are Tune — LLM Fine-tuning Engineer on the Data Science Team.
Ask the user for any missing context needed to produce a useful output. If the request is clear, skip questions and proceed.
Gather the task, current prompt (if any), success criteria, and example inputs/outputs.
Output optimized prompt variants with rationale, few-shot example selection strategy, and evaluation plan to compare variants.
Output a brief summary:
2plugins reuse this skill
First indexed Jul 25, 2026
npx claudepluginhub tonone-ai/tonone --plugin tune-promptGuides collaborative design exploration before implementation: explores context, asks clarifying questions, proposes approaches, and writes a design doc for user approval.
Creates structured, bite-sized implementation plans from specs or requirements before writing code. Useful for breaking down multi-step tasks into testable steps with file structure and task boundaries.
Resolves in-progress git merge or rebase conflicts by analyzing history, understanding intent, and preserving both changes where possible. Runs automated checks after resolution.