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.
Borrowing it
Nothing to install: this file belongs to yohey-w/multi-agent-shogun. 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/yohey-w/multi-agent-shogun/main/.cursor/skills/inbox-write/SKILL.mdgit 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/inbox-write)<a href="https://agentmods.dev/skills/yohey-w/multi-agent-shogun/inbox-write"><img src="https://agentmods.dev/badge/skills/yohey-w/multi-agent-shogun/inbox-write.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 | $0.00032 | $0.00322 |
| Opus 5 | $0.00016 | $0.00161 |
| Sonnet 5 | $0.00006 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
inbox-write 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
他のエージェントにメッセージを送るには、必ずこのスキルを使うこと。 tmux send-keys で直接メッセージを送ることは禁止。
使い方
bash scripts/inbox_write.sh <target_agent> "<message>" <type> <from>
type 一覧
| type | 用途 |
|---|---|
cmd_new |
新規コマンド（shogun→karo） |
task_assigned |
タスク割り当て（karo→ashigaru） |
report_received |
作業完了報告（ashigaru→karo/gunshi） |
clear_command |
セッションリセット指示 |
model_switch |
モデル切り替え指示 |
例
bash scripts/inbox_write.sh karo "cmd_048を書いた。実行せよ。" cmd_new shogun
bash scripts/inbox_write.sh ashigaru3 "タスクYAMLを読んで作業開始せよ。" task_assigned karo
bash scripts/inbox_write.sh gunshi "足軽5号、任務完了。品質チェックを仰ぎたし。" report_received ashigaru5
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 · 32 lines · 32 tokens per session scan A 7ed93663a827
inbox-write is a skill published in the GitHub repository yohey-w/multi-agent-shogun (1,420 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 322 once invoked, about $0.0002 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.