Evaluating Code Models
This skill assesses code model outputs for correctness and quality, ensuring generated code meets specified requirements effectively.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add zechenzhangagi-evaluating-code-models npx -- -y @trustedskills/zechenzhangagi-evaluating-code-models
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"zechenzhangagi-evaluating-code-models": {
"command": "npx",
"args": [
"-y",
"@trustedskills/zechenzhangagi-evaluating-code-models"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill evaluates code models, providing assessments of their performance. It can analyze code generation quality and identify potential issues within the model's output. The tool aims to provide insights into a code model’s strengths and weaknesses for research purposes.
When to use it
- Analyzing newly trained code generation models to understand their capabilities.
- Comparing different code models on specific tasks or datasets.
- Identifying areas where a code model needs improvement based on its generated output.
- Debugging issues in existing code models by assessing the quality of their generated code.
Key capabilities
- Code Model Evaluation
- Performance Assessment
- Quality Analysis
- Research Insights
Example prompts
- "Evaluate this Python code generation model's performance on a sorting algorithm task."
- "Analyze the output of this code model and identify any potential errors or inefficiencies."
- “Compare the generated code quality between Model A and Model B for a simple web server implementation.”
Tips & gotchas
This skill is intended primarily for research purposes. It may require familiarity with code models and evaluation metrics to interpret results effectively.
Tags
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