MR
Mayur Rathi
@sickn33
⭐ 39.1k GitHub stars

llm-prompt-optimizer

llm-prompt-optimizer是一款data方向的AI技能,核心价值是Use when improving prompts for any LLM,可用于解决开发者在data领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.

Last verified on: 2026-05-30
mkdir -p ./skills/llm-prompt-optimizer && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/llm-prompt-optimizer/SKILL.md -o ./skills/llm-prompt-optimizer/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.

🎯 Best For

  • Claude users
  • Data professionals
  • Analytics teams
  • Researchers

💡 Use Cases

  • Data pipeline auditing
  • Query optimization

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply llm-prompt-optimizer to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

How do I install llm-prompt-optimizer?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/llm-prompt-optimizer/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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