Smart Auto Updater

**Quick judgment** Smart Auto Updater is a solid fit for teams that want semi-autonomous OpenClaw maintenance with conservative safety measures and human-readable reporting. It’s best treated as an intelligent assistant for updates rather than a fully trusted change-management oracle. **Key strengths** - Automates discovery of OpenClaw and skill updates using `clawhub update --dry-run`. - Uses an LLM to analyze changelogs/diffs and classify risk (HIGH/MEDIUM/LOW) with architecture/performance/compatibility signals. - Policy engine maps risk levels to actions (auto-update vs. skip + report), configurable via environment variables. - Clear report formats suitable for operators (including impact assessment and recommendations). - Designed for cron/job integration and includes troubleshooting guidance and reference docs. **Main risks / limitations** - Risk classification is only as good as the LLM and prompts; misclassification can still occur, especially on complex or poorly documented changes. - Requires a working AI model backend (`SMART_UPDATER_MODEL` or default) and network access to ClawHub, introducing more failure points. - Performance overhead from LLM calls may be noticeable for frequent checks or large numbers of skills (though usually acceptable for scheduled jobs). - Does not eliminate the need for proper staging/rollout; HIGH-risk decisions still demand human review. **Recommended scenarios** - **Production**: Use with conservative settings (e.g., `SMART_UPDATER_AUTO_UPDATE=LOW`, `SMART_UPDATER_RISK_TOLERANCE=HIGH`), focusing on auto-applying clearly low-risk fixes and generating detailed reports for everything else. - **Staging / non-critical environments**: Loosen risk tolerance (e.g., auto-update LOW+MEDIUM) to accelerate validation of updates before they reach production. - **Ops/SRE teams with many skills**: Use as a central scheduled updater+reporter to reduce manual changelog reading while keeping a human in the loop for HIGH-risk changes.