upskill

🥈Silver

Upskill generates and evaluates agent skills for code agents like Claude Code, Open Code, and OpenAI Codex. It benefits operations teams by creating skills with teacher models and optimizing them with student models. It connects to code agents and workflows requiring automated skill evaluation.

481100Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/huggingface/upskill.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/huggingface/upskill

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Create a new skill for [CODE_AGENT] that can [SPECIFIC_TASK]. The skill should include [REQUIRED_COMPONENTS] and follow these guidelines: [GUIDELINES]. Evaluate the skill's performance using [EVALUATION_METRICS] and suggest improvements based on the results.

Example Output

Skill Name: Data Cleaning Expert

Description: This skill enables the code agent to clean and preprocess datasets efficiently. It includes components for handling missing values, removing duplicates, and standardizing data formats.

Performance Evaluation:
- Accuracy: 92%
- Speed: 1.2 seconds per record
- Resource Usage: Low

Suggested Improvements:
1. Implement more advanced algorithms for handling missing values.
2. Optimize the duplicate removal process to reduce processing time.
3. Add support for additional data formats to increase versatility.

Detailed Evaluation Report:
The skill performed well in handling standard datasets but struggled with highly irregular data formats. The duplicate removal process was efficient but could be further optimized. Overall, the skill is ready for deployment but would benefit from the suggested improvements.