Record execution trajectories and gather rewards to optimize agent performance.
npx skills add https://github.com/sandmark78/workspace --skill agent-optimizer
Please help me install this Agent Skill. Skill: agent-optimizer Source: https://github.com/sandmark78/workspace/tree/main/skills/agent-optimizer Command: npx skills add https://github.com/sandmark78/workspace --skill agent-optimizer
Optimizes agent performance by recording execution trajectories and gathering rewards to drive continuous improvement.
Initialize the optimizer in your agent workspace and run the provided scripts to start recording trajectories.
High-intent search queries and answers about installing and using this skill.
Agent performance optimization uses trajectory recording to log task inputs, tool usage, and timings, then gathers reward signals to analyze trends and guide continuous improvement across federated environments.
Reward shaping applies multiple reward signals to execution trajectories, analyzing trends to guide optimization. Trajectory tracking logs task inputs, outputs, and tool usage timings to provide the data needed for this analysis.
Prompt optimization iterates via A/B tests to compare performance and maintains version history, enabling safe rollbacks to previous prompt versions if the new configurations underperform during execution.
Yes, trajectory tracking and performance analytics apply to federated agent environments and tutorial or data-collection tasks, computing metrics like average reward, success rate, and ROI without external dependencies.
No external dependencies are required beyond the Python standard library and a configured workspace. You can initialize the optimizer in your agent workspace and run provided scripts to start recording trajectories.
Performance analytics computes average reward, success rate, and ROI by recording execution trajectories. Running the provided scripts in your configured workspace automatically logs the necessary data to identify improvement opportunities.
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