autonomous-agent-harness
Transform OpenAI Codex into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging OpenAI Codex's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.
npx skills add mturac/everything-openai-codex --skill autonomous-agent-harness --agent codex
Same command for any agent — swap --agent for claude-code, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Autonomous Agent Harness Turn OpenAI Codex into a persistent, self-directing agent system using only native features and MCP servers. ## Consent and Safety Boundaries Autonomous operation must be explicitly requested and scoped by the user. Do not create schedules, dispatch remote agents, write persistent memory, use computer control, post externally, modify third-party resources, or act on private communications unless the user has approved that capability and the target workspace for the current setup. Prefer dry-run plans and local queue files before enabling recurring or event-driven actions. Keep credentials, private workspace exports, personal datasets, and account-specific automations out of reusable ecc artifacts. ## When to Activate - User wants an agent that runs continuously or on a schedule - Setting up automated workflows that trigger periodically - Building a personal AI assistant that remembers context across sessions - User says "run this every day", "check on this regularly", "keep monitoring" - Wants to replicate functionality from Hermes, AutoGPT, or similar autonomous agent frameworks - Needs computer use combined with scheduled execution ## Architecture ``` ┌
- Consent and Safety Boundaries
- When to Activate
- Architecture
- Core Components
- 1. Persistent Memory
- 2. Scheduled Operations (Crons)
- 3. Dispatch / Remote Agents
- 4. Computer Use
- 5. Task Queue
- Replacing Hermes
- Setup Guide
- Step 1: Configure MCP Servers
- Step 2: Create Base Crons
- Step 3: Initialize Memory Graph
Trigger from CI/CD curl -X POST "https://api.openai.com/dispatch" \ Trigger from webhook GitHub webhook → dispatch → Codex agent → fix → PR Trigger from another agent codex -p "Analyze the output of the security scan and create issues for findings" Daily morning briefing codex -p "Create a scheduled task: every weekday at 9am, review my GitHub notifications, open PRs, and calendar. Write a morning briefing to memory." Continuous learning codex -p "Create a scheduled task: every Sunday at 8pm, extract patterns from this week's sessions and update the learned skills."
What does the autonomous-agent-harness skill do?
Transform OpenAI Codex into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging OpenAI Codex's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.
How do I install it?
Run `npx skills add mturac/everything-openai-codex --skill autonomous-agent-harness --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mturac/everything-openai-codex, a repository with 84 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.