hugging-face-evaluation
hugging-face-evaluation是一款data方向的AI技能，核心价值是Add and manage evaluation results in Hugging Face model cards，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。
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations
mkdir -p ./skills/hugging-face-evaluation && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/hugging-face-evaluation/SKILL.md -o ./skills/hugging-face-evaluation/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations
🎯 Best For
- Claude users
- Data professionals
- Analytics teams
- Researchers
💡 Use Cases
- Data pipeline auditing
- Query optimization
📖 How to Use This Skill
- 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
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply hugging-face-evaluation to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 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 hugging-face-evaluation?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/hugging-face-evaluation/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.