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/worker-internal-workflownpx skills add agentscope-ai/AgentTeams --skill worker-internal-workflowgit 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/worker-internal-workflow)<a href="https://agentmods.dev/skills/agentscope-ai/agentteams/worker-internal-workflow"><img src="https://agentmods.dev/badge/skills/agentscope-ai/agentteams/worker-internal-workflow.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 | $0.00052 | $0.02250 |
| Opus 5 | $0.00026 | $0.01125 |
| Sonnet 5 | $0.00010 | $0.00450 |
| Haiku 4.5 | $0.00005 | $0.00225 |
Grade A, and why
workerflow-internal-workflow 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 5d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Worker Internal Workflow
Use this skill before splitting work inside a single Worker. WorkerFlow is not TeamHarness delegation.
Decision
| Need | Path |
|---|---|
| Current Worker can do it directly | No subagent |
| Same Worker needs short parallel inspection | QwenPaw native subagent |
Same Worker needs visible fan-out to custom roles, workspaces, AGENTS.md, or skills |
Dynamic workflow via worker_agentflow workflow_run |
| Same Worker needs one manually controlled temporary subagent | create_temp_agent directly |
| Work needs a persistent team member or Leader acceptance | Do not use WorkerFlow; use the team workflow |
Native Subagent
Use QwenPaw native subagents for short internal parallelism. The current Worker still owns the final result.
Do not create a temporary agent when no custom prompt, workspace, or skill set is required.
Temporary QwenPaw Agent
Use worker_agentflow with workflow_run when a custom AgentSpec-style
template has already been placed in the default QwenPaw workspace:
<QWENPAW_WORKING_DIR>/workspaces/default/subagents/<role>/
AGENTS.md
PROFILE.md # optional
SOUL.md # optional
skills/
<skill-id>/
SKILL.md
Use list_subagents first when you need to discover the templates available in
the default workspace. Use create_temp_agent directly only when you need
manual control outside a dynamic workflow.
Subagent template rules:
AGENTS.mdshould tell the temporary agent to begin analysis immediately.AGENTS.mdshould forbid greetings, self-introductions, and capability summaries.AGENTS.mdshould define the subagent's focus area so the Worker can send the complete source input instead of manually slicing the input.- When multiple subagent results will be merged, each
AGENTS.mdshould require the same fixed output shape.
Dynamic workflow lifecycle:
- Call
workflow_runwith the explicit current DM/conversationroomId,title, sourceinput, optionalmerge.instruction, and eithersubagentsor DAGnodes. - Each subagent or node must name a default-workspace template with
subagentand a boundedtask. workflow_runstarts the Matrix card, createstmp-...subagents, creates the run-level shared directory, storesworkflow.json, and returnssubmitInstructionsfor nodes that are ready now.- Send each returned
submitPromptto itsagentIdusing the available QwenPaw agent communication tools. - When a subagent finishes, call
workflow_updatewith astepsrow whoseidmatches the node id,statusisdone, andsummarycontains the short result. - For DAG
nodes, inspect everyworkflow_updateresponse. If it returnsreadyInstructions, immediately send each returnedsubmitPromptto itsagentId. - Use
workflow_updateat meaningful phase changes: submitted, running, retrying, merging, cleanup, done, or failed. - Retry a timed-out subagent once when the task is still useful.
- Mark subagents that still fail as missing or failed in the merged result.
- Merge subagent results into the current Worker's own output.
- Delete every temporary agent with
delete_temp_agent. - Call
workflow_finishorworkflow_fail. - After merging, either keep the shared run directory as evidence or remove it
with
cleanup_shared.
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.
- 5d ago First seen · 241 lines · 52 tokens per session scan A aec6fbe706d4
workerflow-internal-workflow is a skill published in the GitHub repository agentscope-ai/AgentTeams (5,564 stars, last pushed yesterday), licensed Apache-2.0. It adds 52 tokens to every session and 2,250 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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