Evaluating Skills for Coding Agents

Robert Xu from LangChain recently shared insights and best practices for evaluating skills, which are fundamental components for improving the performance of coding agents like Codex and Claude Code.

Skills are curated instructions, scripts, and resources that enhance an agent's capabilities in specialized domains. They are dynamically loaded, only when relevant to the task at hand, avoiding overloading the agent with too many tools.

Evaluation Pipeline

The evaluation process is structured in several phases:

  1. Define the tasks that the agent must complete.
  2. Define the skills needed to support those tasks.
  3. Run the agent on the tasks without skills.
  4. Run the agent on the tasks with skills.
  5. Compare performance and iterate on the skills.

Best Practices

  • Clean Testing Environment: The environment in which the agent operates must be consistent and controlled to ensure the reproducibility of tests. The use of Docker or similar sandboxes is recommended.
  • Well-Defined Tasks: Tasks should be specific and measurable, avoiding overly open-ended outputs. A useful approach is to have the agent correct faulty code.
  • Clear Metrics: It is essential to define metrics to quantify the impact of skills, such as the number of tasks completed, the time taken, and the correct invocation of skills.
  • Modularity of Skills: Structuring skills into distinct sections using XML tags facilitates experimentation and A/B testing.
  • Use of AGENTS.md and CLAUDE.md: These files, loaded reliably, are useful for instructing the agent on how and when to use skills.
  • Balancing Content: The name and description of the skills are crucial for the agent. It is important to strike a balance between the number of skills and the amount of content in each.

Monitoring and Observability

To understand the behavior of the agent during testing, it is essential to have good observability. Integration with tools like LangSmith allows you to track every action taken by the agent, making it easier to identify any issues and iterate on the skills.

For those evaluating on-premise deployments, there are trade-offs to consider. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.