agent-team-os

agent-team-os is a skill for Claude Code, Codex from 3338902669-ops/agent-orchestra. It costs 148 tokens per session (1,073 once invoked), scanned A, original, Apache-2.0.

A coordination system for a team of AI agents, or for one agent working in several roles. It provides shared memory, assigned ownership, evidence-backed messages, durable mailboxes, and human approval for irreversible actions.

In plain words
What is it for?
Use it to coordinate agent roles, preserve project knowledge, exchange messages with receipts, and enforce review gates.
Why use it?
It reduces lost context, unclear responsibility, and unsupported conclusions between agents. It also keeps a person in control before actions such as sending, publishing, deleting, deploying, paying, or changing settings.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to coordinate agent roles, preserve project knowledge, exchange messages with receipts, and enforce review gates.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/3338902669-ops/agent-orchestra/agent-team-os
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.

Any agent
npx skills add 3338902669-ops/agent-orchestra --skill agent-team-os
Clone the repo
git clone --depth 1 https://github.com/3338902669-ops/agent-orchestra

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agent-team-os

README.md
[![agentmods](https://agentmods.dev/badge/skills/3338902669-ops/agent-orchestra/agent-team-os/github.svg)](https://agentmods.dev/skills/3338902669-ops/agent-orchestra/agent-team-os)
Your own site
<a href="https://agentmods.dev/skills/3338902669-ops/agent-orchestra/agent-team-os"><img src="https://agentmods.dev/badge/skills/3338902669-ops/agent-orchestra/agent-team-os/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for agent-team-os

Your own site · 80×15
<a href="https://agentmods.dev/skills/3338902669-ops/agent-orchestra/agent-team-os"><img src="https://agentmods.dev/badge/skills/3338902669-ops/agent-orchestra/agent-team-os.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 148 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,073 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00148 $0.01073
Opus 5.5 $0.00059 $0.00429
Sonnet 5.5 $0.00030 $0.00215
Haiku 4.5 $0.00015 $0.00107

Measured 2d ago against content hash 4b6620c8064c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agent-team-os 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 2d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/check.py, templates/scripts/doorbell.ps1, templates/scripts/mail-helper.ps1), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

agent-team-os/SKILL.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Team OS

A complete operating pattern for a small team of AI agents coordinated by one hub, designed so that work is remembered, verifiable, and never silently lost — and so that a human stays in charge of anything irreversible.

The four layers

  1. Layered memory — what the team knows. A curated long-term memory file, daily logs, person/group profiles, a temporal knowledge graph, shared rule fragments distributed to every seat, and periodic digests. Each layer has strict rules for what belongs in it. → references/memory-model.md

  2. Governance — how the team is allowed to act. One coordinator is the human's single entry point. A roster assigns one owner (single writer) per resource. Cross-agent relays must carry evidence anchors (path / hash / raw output); no artifact = no conclusion. The verifier is never the implementer. Passing a review gate is not delivery: outward or irreversible actions (send, publish, delete, deploy, pay, change settings) need per-item human approval. → references/governance.md

  3. Identity mailbox — how seats communicate durably. Every seat has a fixed mail identity. Messages carry ids, threads, delivery receipts, and acknowledgements; landed mail is never lost (store-and-forward). File reservations stop two seats editing the same file. Non-MCP seats use a thin helper script. → references/comms-mail.md, template: templates/scripts/mail-helper.ps1

  4. Event-driven doorbell — how seats hear about new mail in real time. No polling anywhere. A watcher on the mailbox storage fires on writes, consumes durable per-recipient delivery events through persistent cursors, and dispatches each recipient's native wake (CLI round, bridge task, task-card CLI, GUI paste, coordinator marker file). → references/doorbell-event-driven.md, template: templates/scripts/doorbell.ps1

Modes

  • Multi-agent mode — several different agents hold the seats.
  • Single-agent mode — one agent runs several sessions, each session plays one seat with the same roster, mailbox, memory layers, and governance. The doorbell simplifies to a session-level trigger. Everything else is identical, so scaling from one agent to many replaces seats, not the system. → references/single-agent-mode.md

Read the full file on GitHub · 94 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 · 94 lines · 148 tokens per session scan A 4b6620c8064c

Subscribe to this mod's changes

agent-team-os is a skill published in the GitHub repository 3338902669-ops/agent-orchestra (93 stars, last pushed 2d ago), licensed Apache-2.0. It adds 148 tokens to every session and 1,073 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-10-23.

Related

Other skills, from other repositories

memory

Persistent agent memory — store, recall, search across sessions.

iii-experimental/agentos · 13 tokens

commonly

You are a member of a Commonly workspace — a shared space where humans and AI agents from any origin collaborate in pods (chat rooms with memory). Use this whenever you are connected to Commonly via the commonly MCP tools: to read what's happening, post, remember things across sessions, react, DM other agents, and…

Team-Commonly/commonly · 0 tokens

lore

Curating cross-agent knowledge and institutional memory: extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices. Use for memory curation.

simota/agent-skills · 40 tokens

squads-learn

Capture learnings after completing work. Use when finishing a task, fixing a bug, discovering a pattern, or learning something worth remembering for future sessions. Helps build institutional memory.

agents-squads/squads-cli · 41 tokens

novel-writing-assets

A manager for reusable writing preferences, such as recurring objects, mistaken assumptions, realistic details, pacing, and dialogue style.

Xiaoyangy/novel-studio · 77 tokens

agent-engineering-expert

Build LLM agents that use tools safely: tool design, the agent loop, memory, MCP servers, multi-agent orchestration, sandboxing and prompt-injection defence. Use when the user mentions AI agents, tool use or function calling, MCP or Model Context Protocol, autonomous workflows, multi-agent systems, LangChain or…

personamanagmentlayer/pcl · 94 tokens