Optimize LLM prompts and append the final prompt to .ai/PROMPT.md.
npx skills add https://github.com/PaulRBerg/agent-skills --skill refine-prompt
Please help me install this Agent Skill. Skill: refine-prompt Source: https://github.com/PaulRBerg/agent-skills/tree/main/skills/refine-prompt Command: npx skills add https://github.com/PaulRBerg/agent-skills --skill refine-prompt
This skill optimizes prompts for LLMs to maximize clarity, structure, and actionable results.
Use the refine-prompt skill to optimize the prompt "Summarize quarterly results and outline next steps" and append the result to .ai/PROMPT.md.
High-intent search queries and answers about installing and using this skill.
Optimizing prompts for LLMs involves structuring them with clear role assignments, few-shot examples, and explicit output formats to maximize clarity and actionability. This skill applies these principles systematically and saves the refined prompt to .ai/PROMPT.md for reuse across your project.
Few-shot prompt design includes providing example input-output pairs and explicit instructions for the task. This skill generates deployment-ready prompts with few-shot patterns built in, then persists them to .ai/PROMPT.md so your team can reference and iterate on consistent prompt structures.
Yes. This skill generates a complete prompt block and appends it to .ai/PROMPT.md, creating a centralized artifact your project can reference. This approach keeps prompts versioned and accessible to all AI assistants and team members working on the project.
The skill supports configurable behavior via YAML frontmatter, letting you tailor prompts for specific tasks and model requirements. Configuration controls how the prompt is structured, what role or context is assigned, and what output format is enforced before saving to .ai/PROMPT.md.
Effective prompts combine role clarity, structured examples, and explicit output specifications. This skill applies prompt-engineering best practices to transform ambiguous requests into well-formed, actionable prompts that produce consistent, higher-quality results from LLMs.
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