From tune
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: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:
Guides completion of development work by verifying tests, detecting environment, and presenting structured options for merge, PR, or cleanup.
Guides creation and editing of skills using test-driven development with pressure scenarios and subagents to verify agent compliance.
Dispatches multiple subagents concurrently for independent tasks without shared state. Use when facing 2+ unrelated failures or subsystems that can be investigated in parallel.
2plugins reuse this skill
First indexed Jul 25, 2026
npx claudepluginhub tonone-ai/tonone --plugin tune