AgentTeams is a runtime platform where multiple AI agents collaborate in shared Matrix rooms under the coordination of a manager. It is for human-supervised or enterprise workflows that need visible, auditable cooperation among agents running on different runtimes, with shared files and centralized traffic management.
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 skills/agentscope-ai/agentteams/organizationnpx skills add agentscope-ai/AgentTeams --skill organizationgit clone --depth 1 https://github.com/agentscope-ai/AgentTeamsWrote 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/skills/agentscope-ai/agentteams/organization)<a href="https://agentmods.dev/skills/agentscope-ai/agentteams/organization"><img src="https://agentmods.dev/badge/skills/agentscope-ai/agentteams/organization.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 5.1 | $0.00057 | $0.00345 |
| Opus 5 | $0.00028 | $0.00172 |
| Sonnet 5 | $0.00011 | $0.00069 |
| Haiku 4.5 | $0.00006 | $0.00034 |
Grade A, and why
organization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Organization
Use this skill for current AgentTeams topology and runtime state.
Source Of Truth
Use agt CLI when available. Do not infer current state from memory, old chat history, or old task files.
Useful commands:
agt get workers "${AGENTTEAMS_WORKER_CR_NAME:-$AGENTTEAMS_WORKER_NAME}" -o json
TEAM_CR="$(agt get workers "${AGENTTEAMS_WORKER_CR_NAME:-$AGENTTEAMS_WORKER_NAME}" -o json | jq -r '.team')"
agt get workers --team "$TEAM_CR" -o json
Use the Team CR name from your own Worker metadata for team-scoped CLI filters. Do not use a runtime/storage teamName from prompts, task files, or old chat as --team. If team-scoped queries are denied, ask your coordinator instead of guessing.
What To Use It For
- Confirm your coordinator's Matrix ID
- Confirm your team or standalone worker context
- Confirm room IDs when asked to reason about routing
- Check your own Worker phase/runtime if needed
Do not use your Worker profile room or private room as the delivery target for a task result. Task completion routing comes from shared/tasks/{task-id}/meta.json.room_id.
If required identity or room metadata is missing, ask your coordinator. Do not guess.
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.
- 6d ago First seen · 34 lines · 57 tokens per session scan A 2663b9b3d0f1
organization is a skill published in the GitHub repository agentscope-ai/AgentTeams (5,568 stars, last pushed yesterday), licensed Apache-2.0. It adds 57 tokens to every session and 345 once invoked, about $0.0003 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.
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