agent-learner

What This Skill Does

The agent-learner skill acts as an intelligent, persistent laboratory for your AI agent development. It provides a standardized command-line interface for tracking every stage of the prompt engineering lifecycle. By creating a unified logging environment, it allows developers to systematically benchmark, compare, and optimize their prompts and model configurations. Whether you are conducting A/B testing on system instructions, tracking token usage costs, or managing fine-tuning sessions, this skill maintains an audit trail in your local data directory. Its architecture ensures that your experimental data is always accessible, searchable, and exportable, turning subjective AI performance tuning into a data-driven process.

Installation

To add this skill to your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/bytesagain/ba-agent-learner

Ensure that you have the necessary write permissions in your ~/.local/share/ directory, as the skill will create the agent-learner data store there automatically upon its first execution.

Use Cases

• Iterative Prompt Refinement: Use the prompt command to log various system prompt iterations, then use evaluate to track how specific changes affect output quality over time.
• Performance Benchmarking: Automate the logging of benchmark test results for different model versions, allowing you to identify regression points in your agent logic.
• Cost & Usage Auditing: Leverage cost and usage commands to maintain a historical log of token consumption, providing insights into which prompt configurations are the most resource-efficient.
• Behavioral Analysis: Use analyze to document unexpected model behaviors or edge cases encountered during testing, ensuring you have a searchable record for future troubleshooting.

Example Prompts

1. "Benchmark the current 'creative-assistant' prompt against the previous 5 entries in the benchmark.log and give me a summary of the performance trend."
2. "Search through all evaluation results for the term 'hallucination' to see if my recent parameter tweaks have improved accuracy."
3. "Export all my current optimization logs to a CSV file so I can visualize the performance gains in Excel."

Tips & Limitations

• Maintain Consistency: Always include a description when using data-logging commands to ensure the timestamp|value format remains meaningful for future analysis.
• Search Effectively: Since the search command is case-insensitive and operates via standard grep, keep your log entries descriptive to maximize the accuracy of your full-text search results.
• Resource Management: Periodically use stats to monitor your log file sizes. While the skill is lightweight, high-volume benchmarking can generate significant text data over long periods.
• Local Only: Note that this skill is strictly for local file management. It does not perform remote API calls or cloud synchronization, making it a highly secure and private tool for local experiment tracking.