Process CAM system events using a multi-layered decision model.
npx skills add https://github.com/jcyLite/openclaw-plugin-agent-monitor --skill cam-notify
Please help me install this Agent Skill. Skill: cam-notify Source: https://github.com/jcyLite/openclaw-plugin-agent-monitor/tree/main/skills/cam-notify Command: npx skills add https://github.com/jcyLite/openclaw-plugin-agent-monitor --skill cam-notify
This Skill intelligently processes system events from the Code Agent Monitor (CAM), deciding whether to notify users, auto-approve requests, or request human confirmation, ensuring efficient and safe AI agent operation.
permission_request, waiting_for_input, agent_exited, and error.Use the cam-notify skill to process a received CAM system event payload.
High-intent search queries and answers about installing and using this skill.
Automate AI agent permission requests by processing CAM system events through a multi-layered decision model that auto-approves low-risk commands silently and aggregates notifications to reduce noise.
A multi-layered decision model for AI agent risk assessment uses whitelist, blacklist, and LLM judgment layers to evaluate event payloads and determine appropriate auto-approval or notification strategies.
Handle waiting for input and agent exit events by extracting context from the CAM event payload to format clear user messages and determine if human confirmation is required.
Yes, cam-notify supports high-risk bash command blocking by flagging high-risk commands and critical decisions for explicit user confirmation instead of auto-approving them.
Yes, you can use cam-notify for notification aggregation and user reply handling to efficiently manage AI agent operations and process user responses to permission requests.
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