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
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 skills add yohey-w/multi-agent-shogun --skill shogun-bloom-configgit clone --depth 1 https://github.com/yohey-w/multi-agent-shogunWrote 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-bloom-config)<a href="https://agentmods.dev/skills/yohey-w/multi-agent-shogun/shogun-bloom-config"><img src="https://agentmods.dev/badge/skills/yohey-w/multi-agent-shogun/shogun-bloom-config/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-bloom-config"><img src="https://agentmods.dev/badge/skills/yohey-w/multi-agent-shogun/shogun-bloom-config.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 | $0.00086 | $0.05579 |
| Opus 5 | $0.00043 | $0.02789 |
| Sonnet 5 | $0.00017 | $0.01116 |
| Haiku 4.5 | $0.00009 | $0.00558 |
Grade A, and why
shogun-bloom-config 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 12d 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 — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/shogun-bloom-config — Bloom Routing Wizard
Overview
選択肢誘導型インタビューで2問に答えるだけで、最適な capability_tiers 設定を
ready-to-paste 形式で生成する。
Output:
capability_tiersYAML →config/settings.yamlにそのまま貼り付け可available_cost_groups宣言- 固定エージェント推奨モデル(Karo / Gunshi)
- カバレッジギャップ警告(Bloom L6が対応不可の場合など)
When to Use
config/settings.yamlの初期セットアップ- サブスク追加・変更後の再設定
- "capability_tiersってどう設定すればいい?"
/shogun-model-listでモデル一覧を確認した後
Instructions
IMPORTANT: Do NOT output the pattern tables directly. Always ask questions first using AskUserQuestion.
Step 1: Q1 — Claude plan (AskUserQuestion)
Call AskUserQuestion with the following:
question: "Claudeのプランを教えてください。"
header: "Claude Plan"
options:
- label: "Max 20x ($200/月)"
description: "Opus・Sonnet・Haiku全モデル利用可。20倍使用量。Spark Dual運用ならコレ (Recommended)"
- label: "Max 5x ($100/月)"
description: "同上、5倍使用量。コスト重視で十分な量なら。"
- label: "Pro ($20/月)"
description: "Opus・Sonnet・Haiku利用可。使用量は標準。個人利用に十分。"
- label: "Free / なし"
description: "SonnetとHaikuのみ(Opus不可)。L6タスクはギャップが発生する。"
Step 2: Q2 — ChatGPT plan (AskUserQuestion)
Call AskUserQuestion with the following:
question: "ChatGPT(OpenAI)のプランを教えてください。"
header: "ChatGPT Plan"
options:
- label: "なし(Claude onlyで運用)"
description: "Claude枠のみ。シンプル構成。足軽はHaiku4.5が主力。"
- label: "Plus ($20/月)"
description: "gpt-5.3-codex利用可(Spark不可)。L4まで補完できる。"
- label: "Pro ($200/月)"
description: "Spark(1000 tok/s, Terminal-Bench 58.4%) + gpt-5.3(77.3%)利用可。足軽7体の最強構成 (Recommended)"
Step 2.5: Q3 — Rate limit preference (両方契約の場合のみ)
Q1=Pro/Max かつ Q2=Plus または Pro の場合のみ聞く。 両方のサブスクが使える場合、同じBloomレベルをどちらのクォータで処理するか確認する。
Q3a: L3タスク(量産コード生成・テンプレート適用)の優先クォータ
Call AskUserQuestion with:
question: "L1-L3タスク(量産・テンプレート・簡単な実装)はどちらのクォータを優先しますか?"
header: "L3クォータ優先"
options:
- label: "ChatGPT Pro (Spark / gpt-5.3) 優先 (Recommended)"
description: "Spark 1000 tok/s で爆速処理。Claude Max枠を温存してL5-L6に集中。"
- label: "Claude Max (Haiku 4.5) 優先"
description: "Claude枠を均等利用。ChatGPT Pro枠を節約してL4に余裕を持たせる。"
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.
- 12d ago First seen · 518 lines · 86 tokens per session scan A 418c9cdaf4d6
shogun-bloom-config is a skill published in the GitHub repository yohey-w/multi-agent-shogun (1,420 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 5,579 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 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.