agentleFS
0xUrsanomics0xUrsanomics/utopia-os/AGENTS.md

utopia-os

AGENTS.md2 starsChanged 23 days agoMITView on GitHub

Utopia OS is Claude-Code-native. Its canonical agent instructions live in CLAUDE.md. If your harness reads AGENTS.md (opencode, Codex, and others), treat CLAUDE.md as the source of truth and this file as the portability guide. The system splits cleanly into a portable substrate that runs on any harness, and a Claude Code wiring layer that needs a per-harness adapter. The adapter model (portable core vs harness shim, the four dimensions, per-harness mapping) is in docs/adapters.md. Adapters shipped: low-cost (near 1:1), codex…

The file

Files
AGENTS.md

AGENTS.md

Utopia OS is Claude-Code-native. Its canonical agent instructions live in CLAUDE.md. If your harness reads AGENTS.md (opencode, Codex, and others), treat CLAUDE.md as the source of truth and this file as the portability guide.

The system splits cleanly into a portable substrate that runs on any harness, and a Claude Code wiring layer that needs a per-harness adapter.

Portable substrate (runs on any harness)

  • MCP daemon. The tools, memory, and recall are exposed over the Model Context Protocol, an open standard. Any MCP-capable harness (opencode, Cline, Cursor, Zed, Hermes) can point its MCP config at the daemon and use them unchanged.
  • Memory files. Plain Markdown under memory/templates/. No harness owns them.
  • Recall and state machinery. Plain Python under scripts/memory/, callable as a tool or over MCP.
  • Skill and knowledge content. The actual workflows and reference packs are prose, adoptable regardless of how your harness triggers them.

Claude Code wiring (needs a per-harness adapter)

  • CLAUDE.md. The instruction file, in Claude Code's format. On another harness, mirror it into that harness's convention (this AGENTS.md, .cursorrules, and so on).
  • settings.example.json hooks. The automated guardrail and hygiene layer (stand-down gate, send guards, compound counter, auto-logging, save/compact, delivery obligations) is wired as Claude Code hooks: PreToolUse, PostToolUse, Stop, SessionStart, UserPromptSubmit. A harness with no equivalent event system cannot run these automatically. Reimplement them on its hook model, or run the gates manually.
  • SKILL.md frontmatter routing. Skills auto-trigger off frontmatter (the Agent Skills convention shared by Claude Code and Hermes). On a harness without it, the skill content still works; you invoke it manually or wire your own router.

Port cost by harness

HarnessCostWhy
Hermes, OpenClawLowClaude-Code-derived. Share the SKILL.md and hook conventions. Mostly re-pathing.
opencode, CodexMediumSpeak MCP and AGENTS.md, but different skill and hook models. Keep the substrate, rewrite the wiring.
pi, Kimi Code, Kilo Code, grokHigher, variesMCP support is inconsistent and some have no hook layer at all. You get tools and memory over MCP, but the automated guardrails need rebuilding.

Adopting on a non-Claude-Code harness

  1. Point your harness's MCP config at the daemon. Tools, memory, and recall work immediately.
  2. Mirror CLAUDE.md into your harness's instruction file.
  3. Copy the skill and knowledge content. Wire triggering to your harness, or invoke manually.
  4. Reimplement the settings.example.json hooks on your harness's event system. Until then the guardrails do not fire automatically, so run the critical ones (stand-down, send guards) by hand.

The adapter model (portable core vs harness shim, the four dimensions, per-harness mapping) is in docs/adapters.md. Adapters shipped: low-cost (near 1:1), codex (reference), kimi, grok; medium (guardrails become in-process plugins, each ships a plugin scaffold), hermes, openclaw, opencode, pi. Kilo is parked (no lifecycle hook layer). One HOST adapter, centaur, is a deploy guide for running Utopia OS as the Claude Code harness inside a centaur sandbox pod (not a config mapping).

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.