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name, description, license, metadata
| name | description | license | metadata | ||||
|---|---|---|---|---|---|---|---|
| agent-evals | Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. | MIT |
|
Agent Evals
Create repeatable checks so agent behavior improves safely over time.
Evaluation Layers
- Unit evals: prompt-level correctness
- Tool evals: API/tool call decision quality
- End-to-end evals: realistic multi-step tasks
- Safety evals: prompt injection and data leak resistance
CI/CD Integration
# Example eval pipeline steps
make evals-smoke
make evals-regression
make evals-safety
Best Practices
- Version datasets with expected outputs.
- Track pass rates and score drift over time.
- Block deploys on critical safety regressions.
Related Skills
- github-actions - Eval automation in CI
- ai-agent-security - Security-focused eval cases