Self Improving Agent Python

What This Skill Does

The Self Improving Agent Python skill is designed to transform a static AI agent into a dynamic, learning entity. By integrating a multi-layer feedback loop, this skill enables your agent to evaluate its performance after every task, document lessons learned, and formulate optimization plans for future execution. It utilizes a weighted scoring system based on completion, efficiency, quality, and satisfaction to objectively measure success. When performance drops, the system automatically triggers deep reflection, ensuring that mistakes are not repeated and best practices are codified into a central repository.

Installation

To add this capability to your environment, use the OpenClaw command-line interface. Run the following command in your terminal:

clawhub install openclaw/skills/skills/brandon114/self-improving-agent-python

Ensure that your workspace has the necessary read/write permissions, as the skill requires access to the /self-improvement directory to maintain its evaluation logs and knowledge databases.

Use Cases

1. High-Stakes Content Production: Automatically analyze the quality of generated articles, refining the tone or structure based on satisfaction metrics.
2. Workflow Automation: Identify bottlenecks in multi-step scripts by measuring efficiency scores and suggesting alternative, faster execution paths.
3. Continuous Skill Development: Maintain a growing database of 'lessons learned' that the agent can refer back to when encountering complex, multi-variable problems, effectively building a permanent memory of what strategies provide the highest impact.

Example Prompts

1. "I've just finished the data extraction task. Please run an evaluation using the self-improvement skill and let me know if there are efficiency bottlenecks."
2. "We experienced a failure in the API integration earlier; please log a new lesson learned for the 'tools' category so we don't repeat that error."
3. "Run a performance analysis on the last five content creation tasks and suggest an optimization plan for our current workflow."

Tips & Limitations

• Consistency is Key: The accuracy of the performance analysis scales with the number of tasks evaluated. Avoid skipping evaluations for minor tasks, as this creates gaps in the data set.
• Data Integrity: The system relies on the evaluations.json file. Ensure that no manual edits are made to the format of this file to prevent script crashes.
• Learning Lag: The optimization plan relies on historical trends. You should typically run the optimize_agent.py script after a batch of at least 10 tasks to ensure meaningful patterns are identified.