What problem does it solve? Running an AI agent continuously across sessions normally requires standalone frameworks like Hermes or AutoGPT with their own gateways, memory systems, and orchestration layers. This Skill replaces those frameworks by composing Claude Code's native scheduled tasks, dispatch, memory, and MCP servers into a persistent self-directing agent system. ## Core Features & Use Cases - Persistent Memory: Combines built-in markdown memory files with the MCP memory server's knowledge graph for cross-session recall of entities, relations, and observations. - Scheduled Operations: Creates cron-based recurring tasks such as daily PR reviews, hourly health monitors, and nightly test runs using cron expressions. - Dispatch and Computer Use: Triggers remote agents from CI/CD webhooks and automates browsers or desktops through the computer-use MCP server. - Task Queue: Maintains a memory-persisted task list that survives session boundaries. - Use Case: Set up an autonomous PR reviewer that checks watched repositories every 30 minutes, runs tests on new pull requests, posts review comments via the GitHub MCP, and records review status in memory. ## Quick Start Ask the agent to create a scheduled task that reviews open pull requests every weekday at 9am and writes a summary to memory.
