ahmad-ubaidillah/agent-orchestration-improve-agent
aizen-gate/skills-reference/.agent/skills/agent-orchestration-improve-agent/SKILL.md
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
data-ai
Updated Mar 27, 2026
$ install --global
skillsauthnpx skillsauth add ahmad-ubaidillah/aizen-gate agent-orchestration-improve-agentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Mar 30, 2026, 6:49 AM57.4s18 files scanned
SKILL.md
- version:
- 4.1.0-fractal
- name:
- agent-orchestration-improve-agent
- description:
- Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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
- Establish baseline metrics and collect representative examples.
- Identify failure modes and prioritize high-impact fixes.
- Apply prompt and workflow improvements with measurable goals.
- 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.
🧠 Knowledge Modules (Fractal Skills)
1. 1.1 Gather Performance Data
2. 1.2 User Feedback Pattern Analysis
3. 1.3 Failure Mode Classification
4. 1.4 Baseline Performance Report
5. 2.1 Chain-of-Thought Enhancement
6. 2.2 Few-Shot Example Optimization
7. 2.3 Role Definition Refinement
8. 2.4 Constitutional AI Integration
9. 2.5 Output Format Tuning
10. 3.1 Test Suite Development
11. 3.2 A/B Testing Framework
12. 3.3 Evaluation Metrics
13. 3.4 Human Evaluation Protocol
14. 4.1 Version Management
15. 4.2 Staged Rollout
16. 4.3 Rollback Procedures
17. 4.4 Continuous Monitoring
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