smart-auto-updater

What This Skill Does

The smart-auto-updater is an intelligent maintenance agent designed to oversee OpenClaw system health and skill currency. Instead of blindly applying updates, it acts as a gatekeeper by performing a deep impact assessment. It checks for updates in both the core platform and individual skills, evaluates the potential risk level of changes (High, Medium, or Low), and decides autonomously whether to apply the update or skip it based on your defined risk tolerance. By leveraging an LLM to analyze diffs and changelogs, it assesses architectural, performance, and compatibility implications before any system modification occurs, effectively minimizing downtime and regression risks.

Installation

To integrate the smart-auto-updater into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/ruiwang20010702/smart-auto-updater

Once installed, you can configure the behavior using environment variables such as SMART_UPDATER_MODEL, SMART_UPDATER_AUTO_UPDATE, and SMART_UPDATER_RISK_TOLERANCE to align the agent with your specific production or development safety requirements.

Use Cases

• Hands-off Maintenance: Ideal for production environments where you want to keep skills up-to-date with security patches without fearing breaking changes.
• Automated Reporting: Use as a monitoring tool in high-stakes environments where you prefer to manually approve any updates but want a detailed automated impact assessment report before taking action.
• Risk Mitigation: Automatically handle routine, low-risk patches while flagging complex architectural changes for human review.

Example Prompts

1. "Run a smart update check on all installed skills and report any high-risk updates immediately."
2. "Perform an update check; if the risk is LOW, apply the changes, otherwise provide a full impact analysis report."
3. "Check for updates and summarize the changes for my current environment, focusing specifically on performance impacts."

Tips & Limitations

• Configuring Tolerance: Set SMART_UPDATER_RISK_TOLERANCE to 'HIGH' if you prefer to be notified of every change, or 'LOW' if you trust the agent to handle everything autonomously.
• Model Selection: The quality of the analysis is dependent on the LLM model used. Using a high-capability model like MiniMax-M2.1 is recommended for accurate architectural assessment.
• Limitations: The agent relies on provided changelogs and code diffs; if a package maintainer provides poor documentation, the AI analysis may be less effective. Always maintain a backup of your environment before major updates.