agent-orchestration-improve-agent
SolidSystematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
AI & Automation 131 stars
27 forks Updated 1 weeks ago MIT
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# Agent Performance Optimization Workflow
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.]
## Use this skill when
- Improving an existing agent's performance or reliability
- Analyzing failure modes, prompt quality, or tool usage
- Running structured A/B tests or evaluation suites
- Designing iterative optimization workflows for agents
## Do not use this skill when
- You are building a brand-new agent from scratch
- There are no metrics, feedback, or test cases available
- The task is unrelated to agent performance or prompt quality
## Instructions
1. Establish baseline metrics and collect representative examples.
2. Identify failure modes and prioritize high-impact fixes.
3. Apply prompt and workflow improvements with measurable goals.
4. Validate with tests and roll out changes in controlled stages.
## Safety
- Avoid deploying prompt changes without regression testing.
- Roll back quickly if quality or safety metrics regress.
## Phase 1: Performance Analysis and Baseline Metrics
Comprehensive analysis of agent performance using context-manager for historical data collection.
### 1.1 Gather Pe...
Details
- Author
- lingxling
- Repository
- lingxling/awesome-skills-cn
- Created
- 3 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- MIT
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