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:
Guides 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.
2plugins reuse this skill
First indexed Jul 25, 2026
npx claudepluginhub tonone-ai/tonone --plugin tune-prompt