Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/autonomous-ai/autonomous-os/agents-mdgit clone --depth 1 https://github.com/autonomous-ai/autonomous-osWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/autonomous-ai/autonomous-os/agents-md)<a href="https://agentmods.dev/instructions/autonomous-ai/autonomous-os/agents-md"><img src="https://agentmods.dev/badge/instructions/autonomous-ai/autonomous-os/agents-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.03197 | $0.03197 |
| Opus 5 | $0.01598 | $0.01598 |
| Sonnet 5 | $0.00639 | $0.00639 |
| Haiku 4.5 | $0.00320 | $0.00320 |
Grade A, and why
autonomous-os AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides guidance to Codex and other coding agents when working in
this repository. Treat CLAUDE.md as the upstream source of truth; this file is
the Codex-compatible mirror of those project rules.
Multi-IDE Rules
This repo is developed across multiple AI-assisted environments. The following rules apply to all code changes:
-
Update docs on code change - When changing behavior, architecture, or APIs, update both the English and Vietnamese docs. Keep numbers, flows, endpoints, and states accurate with the code. Platform docs are in
docs/; lamp-specific docs are inrobots/lamp/docs/.Platform docs (
docs/+docs/vi/):Code area English doc Vietnamese doc os-server, API, startup docs/os-server.mddocs/vi/os-server_vi.mdSetup flow, provisioning docs/setup-flow.mddocs/vi/setup-flow_vi.mdWeb UI, configuration pages docs/web-ui.mddocs/vi/web-ui_vi.mdFlow Monitor (turn pipeline, JSONL, SSE) docs/flow-monitor.mddocs/vi/flow-monitor_vi.mdOverall structure docs/overview.mddocs/vi/overview_vi.mdMQTT, dispatch, publish docs/mqtt.mddocs/vi/mqtt_vi.mdOTA, bootstrap docs/bootstrap-ota.mddocs/vi/bootstrap-ota.mdSpeech emotion recognition (SER) docs/speech-emotion.mddocs/vi/speech-emotion_vi.mdRealtime voice agent (HAL realtime, Gemini Live / OpenAI Realtime, delegate)docs/realtime-voice.mddocs/vi/realtime-voice_vi.mdPerception service (cloud DL inference), load balancer, encryption, models docs/perception-service.mddocs/vi/perception-service_vi.mdHermes agent backend ( agent_runtime, runtimes/hermes)docs/agentic/hermes.mddocs/vi/agentic/hermes_vi.mdPicoClaw agent backend ( agent_runtime, runtimes/picoclaw, WebSocket)docs/agentic/picoclaw.mddocs/vi/agentic/picoclaw_vi.mdAdding/changing an agentic backend (AgentGateway contract, switch, install/presync, migration, skills, hooks, reset) docs/agentic/adding-agent-runtime.mddocs/vi/agentic/adding-agent-runtime_vi.mdSafety engine (SAFETY.md bounds, deterministic enforcement gate) docs/safety.mddocs/vi/safety_vi.md
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 268 lines · 3,197 tokens per session scan A 4b40726076e4
autonomous-os AGENTS.md is an instructions file published in the GitHub repository autonomous-ai/autonomous-os (266 stars, last pushed yesterday), licensed Apache-2.0. It adds 3,197 tokens to every session, about $0.0160 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
rosclaw AGENTS.md
AGENTS.md instructions for ros-claw/rosclaw, covering rosclaw agent instructions, runtime boundary, tool policy, robot integration setup and capability apps.
rosclaw CLAUDE.md
Claude Code instructions for ros-claw/rosclaw, covering claude.md — rosclaw project onboarding, rosclaw runtime boundary (managed), safety contract (p0), robot integration setup and capability apps.
worldforge AGENTS.md
AGENTS.md instructions for AbdelStark/worldforge, covering agent guide, project identity, architecture map, tech stack and commands.
inspect-robots CLAUDE.md
Claude Code instructions for robocurve/inspect-robots, covering inspect robots — agent guide, the one big idea, layout, working here and out of scope (separate repos / plugins).
agenticros AGENTS.md
AGENTS.md instructions for agenticros/agenticros, covering agenticros, what this is, important: controlling the robot from codex, architecture and key source files.
worldforge CLAUDE.md
Claude Code instructions for AbdelStark/worldforge: WorldForge is a harness framework for building world-model-based workflows for physical AI. It is the application builder's counterpart to model-training stacks like Stable World Model: it helps roboticists and physical-AI builders compose, evaluate, and benchmark…