multi-agent-shogun is a system that coordinates multiple AI coding command-line agents through a hierarchy of managers, strategists, and workers. Developers use it to split coding requests into parallel tasks and monitor their execution through tmux.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/yohey-w/multi-agent-shogunnpx agentmods add skills/yohey-w/multi-agent-shogun/shogun-agent-statusWrote 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/yohey-w/multi-agent-shogun/shogun-agent-status)<a href="https://agentmods.dev/skills/yohey-w/multi-agent-shogun/shogun-agent-status"><img src="https://agentmods.dev/badge/skills/yohey-w/multi-agent-shogun/shogun-agent-status/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.
<a href="https://agentmods.dev/skills/yohey-w/multi-agent-shogun/shogun-agent-status"><img src="https://agentmods.dev/badge/skills/yohey-w/multi-agent-shogun/shogun-agent-status.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00098 | $0.00647 |
| Opus 5 | $0.00049 | $0.00324 |
| Sonnet 5 | $0.00020 | $0.00129 |
| Haiku 4.5 | $0.00010 | $0.00065 |
Grade A, and why
shogun-agent-status 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 10d 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
/agent-status - エージェント稼働確認
Overview
全エージェントの稼働状態を2つのデータソースから統合判定して一覧表示する。
- Pane状態: tmux capture-paneの末尾5行からCLI固有のidle/busyパターンを検出
- タスクYAML:
queue/tasks/{agent}.yamlのtask_idとstatus - 未読inbox:
queue/inbox/{agent}.yamlの未処理メッセージ数
Claude Code / Codex CLI 両方に対応。
When to Use
- 「稼働確認」「エージェント状態」「布陣確認」と言われた時
- 足軽が暇そうか確認したい時
- タスク配分前に空いているエージェントを探す時
- 誰かが止まっているか調べたい時
Instructions
以下のコマンドを実行する:
bash scripts/agent_status.sh
出力の読み方
| Column | 意味 |
|---|---|
| Agent | エージェント名 |
| CLI | CLI種別（claude/codex） |
| Pane | tmux pane状態: 稼働中/待機中/不在 |
| Task ID | タスクYAMLのtask_id（---=未割当） |
| Status | タスクYAMLのstatus: assigned/done/idle等 |
| Inbox | 未読inboxメッセージ数 |
状態の解釈
- Pane=待機中 + Status=done: 完了済み、次タスク待ち。新タスク配分可能。
- Pane=稼働中 + Status=assigned: 正常にタスク実行中。放置してよい。
- Pane=待機中 + Status=assigned: タスク割当済みだがCLIが止まっている。要調査。
- Pane=稼働中 + Status=done: タスク完了後に別作業中（inbox処理等）。
- Inbox > 0: 未読メッセージあり。エージェントが処理していない可能性。
- Pane=不在: tmux paneが存在しない（shutsujin未実行 or pane killed）。
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.
- 10d ago First seen · 52 lines · 98 tokens per session scan A a30a769ee4a5
shogun-agent-status is a skill published in the GitHub repository yohey-w/multi-agent-shogun (1,420 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 647 once invoked, about $0.0005 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 skills, from other repositories
skill-agent-mapping
This skill should be used when looking up which agents own or consume specific skills, understanding skill-agent relationships, or routing tasks based on skill ownership.
exploration-strategy
This skill should be used when exploring codebases, finding patterns, searching for code, gathering context, or understanding code structure before planning or implementation.
prompt-refinement
This skill should be used when the user provides a vague request, asks to clarify requirements, structure a task, or refine a prompt for multi-agent orchestration.
team-decision
This skill should be used when deciding whether to use Agent Teams for parallel execution or sequential subagent orchestration, based on task analysis, independence criteria, and cost-benefit.
agent-behavior-constraints
This skill should be used when handling agent model selection, tool access permissions, behavioral guardrails, MCP tool preferences, or any question about what agents can/cannot do.
agentsview-usage
Search prior session history to recall how similar work was handled before. Use when you want to leverage a past approach, check prior experience on a topic, answer "how was this handled before", or cross-verify current handling against precedent from earlier sessions.