From tune-recon
Audits existing fine-tuning or prompt engineering work to identify quality gaps and optimization opportunities.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tune-recon:tune-reconThis 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.
Read existing fine-tuning scripts, prompt templates, or evaluation code.
Report: methodology gaps, dataset quality issues, missing evaluation, and whether fine-tuning is justified vs prompting.
Output a brief summary:
2plugins reuse this skill
First indexed Jul 25, 2026
npx claudepluginhub tonone-ai/tonone --plugin tune-reconGuides 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.