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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
Passed 19 of 22 test cases with the skill, and 17 of 22 without it.
2 fixed17 passed either way3 still failing0 broke
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One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to add few-shot examples to our agent prompt. Is it sufficient to just provide a single input-output example without explaining why the output is correct or including failure cases?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends including annotated explanations for why examples succeed or fail | Missed | Passed |
| Includes both positive and negative example pairs to clarify boundary conditions | Missed | Passed |
The instructions your model reads when the skill loads.
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
[Extended thinking: Agent optimization requires a data-driven approach combining performance metrics, user feedback analysis, and advanced prompt engineering techniques. Success depends on systematic evaluation, targeted improvements, and rigorous testing with rollback capabilities for production safety.]
Comprehensive analysis of agent performance using context-manager for historical data collection.
Use: context-manager
Command: analyze-agent-performance $ARGUMENTS --days 30
test cases passed, vs 17 / 22 without
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