Evaluate and rewrite prompts to improve specificity and safety.
npx skills add https://github.com/iamtatsuki05/dotfiles --skill prompt-tuner
Please help me install this Agent Skill. Skill: prompt-tuner Source: https://github.com/iamtatsuki05/dotfiles/tree/main/dotfiles/.agent/skills/prompt-tuner Command: npx skills add https://github.com/iamtatsuki05/dotfiles --skill prompt-tuner
Tuning prompts improves AI output quality by optimizing clarity, intent, and alignment with user goals.
Provide your target prompt, objective, and constraints, then ask the AI to begin an iterative tuning cycle.
High-intent search queries and answers about installing and using this skill.
Tune LLM prompts by applying an iterative workflow of evaluation and rewriting to improve clarity, intent, and alignment with your goals. This structured process ensures accurate and reliable AI responses across various domains.
The best way to rewrite system prompts is through scenario-based tuning that enhances specificity and safety. Using a reproducible workflow with evaluation criteria and guardrails optimizes prompt quality for tasks like coding or summarization.
Yes, you can use prompt tuning for different AI tasks like reasoning and coding. Scenario-based tuning applies structured guidance and evaluation criteria to optimize user templates and instructions across diverse domains.
Prompt evaluation improves safety by applying guardrails and specific rewriting criteria to the instructions. This process ensures the tuned prompts align with user constraints and produce reliable, controlled AI outputs.
Yes, you need to provide your target prompt, objective, and constraints to start an iterative tuning cycle. Supplying these inputs allows the AI to apply evaluation criteria and generate optimized, reproducible prompt variations.
Your LLM output lacks specificity because user templates often require structured prompt tuning to align intent with goals. Applying an iterative evaluation and rewriting workflow optimizes the template for sharper, more accurate responses.
yu13130122297Identify uncertainties in research framing, datasets, metrics, and baselines.
JeffallanDesign and optimize LLM prompts with structured outputs and evaluation frameworks.
jshsakuraRefine prompts to enforce task boundaries, output contracts, and safety constraints.
ovachieverOptimize LLM preference alignment without a reference model using comparison data.
