Borrowing it
Nothing to install: this file belongs to disler/learning-cmux-with-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/disler/learning-cmux-with-agents/main/.claude/commands/spawn-fs-team.mdgit clone --depth 1 https://github.com/disler/learning-cmux-with-agentsWrote 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/commands/disler/learning-cmux-with-agents/spawn-fs-team)<a href="https://agentmods.dev/commands/disler/learning-cmux-with-agents/spawn-fs-team"><img src="https://agentmods.dev/badge/commands/disler/learning-cmux-with-agents/spawn-fs-team.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 | $0.00076 | $0.02663 |
| Opus 5 | $0.00038 | $0.01332 |
| Sonnet 5 | $0.00015 | $0.00533 |
| Haiku 4.5 | $0.00008 | $0.00266 |
Grade A, and why
spawn-fs-team 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 7d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spawn Full-Stack Team
Purpose
You are an orchestrator running in a terminal (not inside cmux). Boot a fresh
5-agent team for Flotion as a new workspace in the cmux window and, if given a
feature, hand it to the team's lead. One team = one workspace = one feature,
and teams share a window — reuse the window that's already open and only create
a new one if cmux has none. You do this by driving the cmux CLI yourself — there
is no script; this command is the recipe.
Variables
TEAM: $1 # short slug for the team; default "fs-team" if omitted FEATURE: $2 # everything after the slug — optional feature to ship now
Model blend (reasoning roles on GLM-5.2, build/verify on Minimax-M3)
LEAD_MODEL: openrouter/z-ai/glm-5.2 PLAN_MODEL: openrouter/z-ai/glm-5.2 BE_MODEL: openrouter/minimax/minimax-m3 FE_MODEL: openrouter/minimax/minimax-m3 TEST_MODEL: openrouter/minimax/minimax-m3
ROLES_DIR: .claude/agents # lead.md, plan.md, build-be.md, build-fe.md, test.md ROSTER: .team/.roster.json
Codebase Structure
.claude/agents/*.md # the 5 role system prompts each teammate boots with
.team/ # shared memory: <team>.roster.json, backlog.md, <role>.md notes
apps/flotion/ # the app the team builds (Vue 3 + TS / FastAPI + SQLite)
Instructions
- Learn the verbs first. Run
cmux --help(and consult thecmuxskill if available). The whole boot is just these verbs:new-window,workspace create,new-split <dir>,rename-tab,workspace-action,set-status,send,send-key,read-screen,workspace close. - You are outside cmux; the socket is in
allowAllmode, so yourcmuxcalls drive it directly. Confirm withcmux identify --jsonbefore starting. - If cmux isn't running, start it — don't stop. A refused socket
(
Connection refused) just means the app isn't up yet. Runopen -a cmux, wait for the socket, then carry on. Only abort if it never comes online. sendtypes,send-key entersubmits. There are no Ctrl-chords; to stop a pane,close-surfaceit.- Capture refs as you create them.
workspace create --jsonreturnsworkspace_ref+ the lead'ssurface_ref; eachnew-split --jsonreturns the newsurface_ref. Thread these through — never guess refs. - Launch each agent by typing its
pi …line into its pane (viacmux sendsend-key enter), not from your own shell.
- Reuse the open window; one team = one workspace. If cmux already has a
window open, add this team as a new workspace inside that existing window —
do not open another OS window. Only run
new-windowwhen cmux has no window at all. Multiple teams coexist as sibling workspaces in one window; each team is its own workspace (its lead + 2×2 workers). - The lead drives the workers; you drive only the lead. Keep every agent observable — read panes, don't assume.
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.
- 7d ago First seen · 176 lines · 0 tokens per session scan A 97e042cfce2f
spawn-fs-team is a command published in the GitHub repository disler/learning-cmux-with-agents (109 stars, last pushed 2mo ago), licensed MIT. It adds 76 tokens to every session and 2,663 once invoked, about $0.0004 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.