oma AGENTS.md

A guide to Open Managed Agents, a system where configurable agents run conversations in sessions inside defined computing environments and use secure credential stores.

In plain words
What is it for?
Use it to understand agent configuration, versioning, session state, real-time streaming, archived sessions, reusable environments, and vaults for secrets.
Why use it?
It explains how agents, conversations, environments, and credentials fit together, which helps developers reason about the system's structure and lifecycle.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/duyet/oma/agents-md
Clone the repo
git clone --depth 1 https://github.com/duyet/oma

Made for: Codex, OpenCode.

Per session 26,280 This file is loaded in full into every session.
When invoked 26,280 The same file — it is already loaded in full.
Security scan A 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.26280 $0.26280
Opus 5 $0.13140 $0.13140
Sonnet 5 $0.05256 $0.05256
Haiku 4.5 $0.02628 $0.02628

Measured 2d ago against content hash fd1ded87d690, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

oma AGENTS.md scanned grade A with 2 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

| **web_fetch** | URL → markdown | Fetches a URL, converts HTML/PDF/DOCX/etc. to markdown via Workers AI `env.AI.toMarkdown()`. When `agent.aux_model` is set, large pages (>5KB) are summarized by the aux model and the fu

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

`child_process`, and no runtime dynamic-import resolution:
AGENTS.md · 2,214 lines

How it starts

The opening of the file, as written. The whole thing — 2,214 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agents Guide

This document covers the core concepts, lifecycle, and configuration of agents in Open Managed Agents.


Core Concepts

Open Managed Agents is built around a meta-harness architecture with four key abstractions:

Agent

An agent is a configuration object that defines what an AI assistant can do. It specifies the model, system prompt, available tools, skills, and optional connections to other agents or MCP servers.

Agents are versioned — every update creates a new version. Sessions bind to a specific agent version at creation time.

Session

A session is a running conversation between a user and an agent. It owns an append-only event log stored in a Durable Object backed by SQLite. Sessions are the unit of state — agents themselves are stateless configurations.

Sessions can be streamed in real-time via SSE, resumed after crashes, and archived when complete.

Environment

An environment defines the execution sandbox — what packages are installed, what networking is allowed, and what container image to use. Environments are reusable across sessions and agents.

Vault

A vault is a secure credential store. Credentials in vaults are never exposed to sandboxes — they're injected via an outbound proxy that intercepts HTTP requests and adds authentication headers transparently.


Agent Lifecycle

                    ┌──────────┐
                    │  Create   │  POST /v1/agents
                    └────┬─────┘
                         │
                    ┌────▼─────┐
              ┌────►│  Active   │◄────┐
              │     └────┬─────┘     │
              │          │           │
         ┌────┴───┐ ┌───▼────┐ ┌───┴─────┐
         │ Update  │ │ Archive│ │ Sessions│
         │ (new    │ │        │ │ use it  │
         │ version)│ └───┬────┘ └─────────┘
         └─────────┘     │
                    ┌────▼─────┐
                    │ Archived  │
                    └──────────┘
  1. CreatePOST /v1/agents with name, model, system prompt, and tools
  2. Use — Create sessions referencing the agent by ID
  3. UpdatePUT /v1/agents/:id creates a new version; existing sessions keep their original version
  4. ArchivePOST /v1/agents/:id/archive soft-deletes the agent

Read the full file on GitHub · 2,214 lines

Changes

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.

  1. 2d ago First seen · 2,214 lines · 26,280 tokens per session scan A fd1ded87d690

Subscribe to this mod's changes

oma AGENTS.md is an instructions file published in the GitHub repository duyet/oma (5 stars, last pushed 13d ago), licensed Apache-2.0. It adds 26,280 tokens to every session, about $0.1314 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.