Runtime Self-Profiling and Optimization
CommunitySelf-optimizes AI agents with live performance.
Software Engineering#performance#metrics#agents#memory#token-efficiency#self-optimization#adaptive-rules
Author4citeB4U
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Continuously evaluating AI agents' performance, identifying wasteful patterns, and updating operating rules to improve speed, accuracy, and token efficiency.
Core Features & Use Cases
- Continuous performance measurement: tokens, latency, errors, and tool calls are tracked to detect waste.
- Self-guided optimization: patterns are identified and operating guidelines updated to prevent repeated issues.
- Persistent learning loop: applies episodic/semantic memory to refine strategies across sessions for immediate impact.
Quick Start
Run a new session and apply the top rule from your newly updated self-optimization guide.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Runtime Self-Profiling and Optimization Download link: https://github.com/4citeB4U/LeeWay-Agent-Skills/archive/main.zip#runtime-self-profiling-and-optimization Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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