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Skill Optimization

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When to use it

• Your skill has good eval coverage but still misses cases it should trigger on.
• You’ve written a skill manually and want a data-driven refinement pass.
• You want to tune trigger precision without hand-editing text.

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How it works

seed skill ──► optimizer agent proposes patch
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              target agent runs on eval batch
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              grader scores pass / fail
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              gate: accept if score improves by ε
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              repeat for N epochs ──► export best

1. The optimizer agent reads the current skill and a summary of failing cases, then proposes a text patch.
2. The target agent runs the patched skill against a batch of eval cases.
3. The gate compares the new score to the current best. If the improvement clears gate_epsilon, the patch is accepted.
4. The run directory records every step’s artifacts for inspection.

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Key concepts

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Quick summary of commands

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See also

• Setup: eval data and grading
• Configuration reference
• Running and monitoring
• Inspecting and exporting results