Dspy Evaluation Suite
The Dspy Evaluation Suite assesses and compares different diffusion models’ performance, streamlining model selection for optimal results.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add dspy-evaluation-suite npx -- -y @trustedskills/dspy-evaluation-suite
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"dspy-evaluation-suite": {
"command": "npx",
"args": [
"-y",
"@trustedskills/dspy-evaluation-suite"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The dspy-evaluation-suite skill provides a framework for evaluating AI agent performance. It allows users to define evaluation datasets, metrics, and scoring functions to systematically assess the quality of agent outputs. This facilitates iterative improvement and comparison between different agents or configurations.
When to use it
- Benchmarking Agent Performance: Compare the effectiveness of multiple AI agents on a standardized task.
- Evaluating New Prompts/Agents: Quickly determine if changes to prompts or new agent versions improve overall performance.
- Identifying Failure Modes: Pinpoint specific areas where an agent consistently struggles, enabling targeted refinement.
- Automated Regression Testing: Integrate evaluation into your development pipeline to ensure that updates don’t degrade existing capabilities.
Key capabilities
- Evaluation dataset definition
- Metric specification
- Scoring function implementation
- Agent output assessment
- Performance reporting
Example prompts
- "Evaluate the agent's performance on the 'question answering' dataset using the 'exact match' metric."
- "Run a regression test for the summarization task and report any significant changes in score."
- "Show me the evaluation results for the latest version of the chatbot, compared to the previous version."
Tips & gotchas
The skill requires familiarity with defining datasets and metrics relevant to your specific use case. Proper configuration of scoring functions is crucial for accurate performance assessment; incorrect setup can lead to misleading results.
Tags
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