Commit Analyzer

What This Skill Does

The Commit Analyzer is a diagnostic tool designed for autonomous agents that utilize Git as their primary ledger for task progression, cognitive development, and skill refinement. By auditing commit metadata, the tool evaluates the 'heartbeat' of the agent. It translates raw commit frequency, temporal patterns, and semantic category distribution (such as Queue, Learning, or Docs) into actionable health metrics. This enables users and developers to distinguish between productive autonomous operation and potential 'dead-lock' scenarios where the agent may be caught in an unproductive loop or idling.

Installation

To integrate this tool into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/bobrenze-bot/commit-analyzer Ensure you have the required Git repository permissions configured, as the tool relies on accessing commit history logs locally to perform its temporal analysis.

Use Cases

1. Performance Auditing: Determine if your agent is hitting the 'Healthy' threshold of 3-6 commits per hour to ensure consistent progress.
2. Meta-Cognition Tracking: Use the category analysis to verify that the agent is maintaining a balanced 1:1 ratio between task execution and active learning.
3. Idle Detection: Automatically identify periods of inactivity or 'waiting mode' to troubleshoot stuck processes or stalled autonomous workflows.
4. Productivity Optimization: Analyze the hourly breakdown to pinpoint the specific times of day when the agent exhibits its highest velocity or 'breakthrough' behavior.

Example Prompts

1. "Perform a quick health check on my agent to see if it has been productive over the last 24 hours."
2. "Show me the category breakdown for the last week to verify if my agent is spending enough time on learning tasks."
3. "Check for any significant idle periods or long gaps in commit activity over the last 48 hours."

Tips & Limitations

The Commit Analyzer is strictly a diagnostic tool and does not resolve issues on its own; it provides the data necessary for human intervention. When interpreting results, consider the context of the work—maintenance periods might naturally show lower commit frequency compared to active feature development. For the most accurate reports, ensure that your commit messages consistently follow a standardized prefixing format (e.g., 'Queue:', 'Learning:', 'Docs:'). Note that results are restricted to local Git repository data.