This is a structured methodology for improving an existing agent when it's underperforming or failing in production. You get a four-phase workflow: baseline metrics and failure analysis, prompt engineering techniques like chain-of-thought and few-shot optimization, A/B testing with statistical validation, and controlled deployment with rollback. The real value is in the systematic approach, especially the failure mode classification and the specific testing framework with blind human evaluation. Use this when you have enough production data to diagnose what's actually broken. If you're just guessing at improvements without metrics or test cases, you're not ready for this workflow yet.
npx -y skills add sickn33/antigravity-awesome-skills --skill agent-orchestration-improve-agent --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
sickn33/antigravity-awesome-skills
moizibnyousaf/ai-agent-skills
github/awesome-copilot