One Karo (manager) coordinating 7 Ashigaru (workers) + 1 Gunshi (strategist) — real session, no mock data.

Quick Start

Requirements: tmux, bash 4+, at least one of: Claude Code / Codex / Copilot / Kimi / OpenCode / Antigravity

git clone https://github.com/yohey-w/multi-agent-shogun
cd multi-agent-shogun
bash first_setup.sh                        # one-time setup: config, dependencies, MCP
source ~/.bashrc                           # reload PATH
claude --dangerously-skip-permissions      # first run only: OAuth + accept Bypass Permissions → /exit
bash shutsujin_departure.sh                # launch all agents

For full install steps (incl. Windows) and the first-30-minutes walkthrough, see 🚀 Quick Start and the basic usage section below.

Type a command in the Shogun pane:

"Build a REST API for user authentication"

Shogun delegates → Karo breaks it down → 7 Ashigaru execute in parallel. You watch the dashboard. That's it.

Want to go deeper? The rest of this README covers architecture, configuration, memory design, and multi-CLI setup.

What is this?

multi-agent-shogun is a system that runs multiple AI coding CLI instances simultaneously, orchestrating them like a feudal Japanese army. Supports Claude Code, OpenAI Codex, GitHub Copilot, Kimi Code, OpenCode, Cursor, and Antigravity.

Why use it?

• One command spawns 7 AI workers + 1 strategist executing in parallel
• Zero wait time — give your next order while tasks run in the background
• AI remembers your preferences across sessions (Memory MCP)
• Real-time progress on a dashboard

        You (上様 / The Lord)
             │
             ▼  Give orders
      ┌─────────────┐
      │   SHOGUN    │  ← Receives your command, delegates instantly
      └──────┬──────┘
             │  YAML + tmux
      ┌──────▼──────┐
      │    KARO     │  ← Distributes tasks to workers
      └──────┬──────┘
             │
    ┌─┬─┬─┬─┴─┬─┬─┬─┬────────┐
    │1│2│3│4│5│6│7│ GUNSHI │  ← 7 workers + 1 strategist
    └─┴─┴─┴─┴─┴─┴─┴────────┘
       ASHIGARU      軍師

Why Shogun?

Most multi-agent frameworks burn API tokens on coordination. Shogun doesn't.

What makes this different

Zero coordination overhead — Agents talk through YAML files on disk. The only API calls are for actual work, not orchestration. Run 8 agents and pay only for 8 agents' work.

Full transparency — Every agent runs in a visible tmux pane. Every instruction, report, and decision is a plain YAML file you can read, diff, and version-control. No black boxes.

Battle-tested hierarchy — The Shogun → Karo → Ashigaru chain of command prevents conflicts by design: clear ownership, dedicated files per agent, event-driven communication, no polling.

Why CLI (Not API)?

Most AI coding tools charge per token. Running 8 Opus-grade agents through the API costs $100+/hour. CLI subscriptions flip this:

"Use AI recklessly" — With flat-rate CLI subscriptions, deploy 8 agents without hesitation. The cost is the same whether they work 1 hour or 24 hours. No more choosing between "good enough" and "thorough" — just run more agents.

Multi-CLI Support

Shogun isn't locked to one vendor. The system supports 7 CLI tools, each with unique strengths:

OpenCode sessions load the agent-specific .opencode/agents/<agent_id>.md definition via --agent and keep automation resets on /new; model changes require a relaunch. Automation uses the repository-provided config/opencode-tui.json via OPENCODE_TUI_CONFIG, which disables app_exit and pins session_interrupt/input_clear to known bindings. Role boundaries are embedded in the generated agent frontmatter: Shogun can read queue/reports/* for oversight but cannot write them, Karo is limited to coordination files plus report aggregation, Ashigaru only touch their own task/report pair, and Gunshi reads ashigaru reports but only writes gunshi_report.yaml.

Antigravity sessions launch with agy --dangerously-skip-permissions. Shogun treats type: antigravity, type: agy, and legacy type: gemini as Antigravity. Authentication and default model selection stay in the host user's Antigravity CLI setup; settings.yaml may optionally pass a concrete model, but auto uses the host default or last-used model.

A unified instruction build system generates CLI-specific instruction files from shared templates:

instructions/
├── common/              # Shared rules (all CLIs)
├── cli_specific/        # CLI-specific tool descriptions
│   ├── claude_tools.md  # Claude Code tools & features
│   ├── copilot_tools.md # GitHub Copilot CLI tools & features
│   ├── opencode_tools.md # OpenCode tools, agent frontmatter, and permission model
│   └── cursor_tools.md  # Cursor Agent tools, skills, and session rules
└── roles/               # Role definitions (shogun, karo, ashigaru)
    ↓ build
CLAUDE.md / AGENTS.md / .github/copilot-instructions.md / .opencode/agents/*.md / .cursor/rules/*.md
  ← Generated per CLI

One source of truth, zero sync drift. Change a rule once, all CLIs get it.

Bottom-Up Skill Discovery

This is the feature no other framework has.

As Ashigaru execute tasks, they automatically identify reusable patterns and propose them as skill candidates. The Karo aggregates these proposals in dashboard.md, and you — the Lord — decide what gets promoted to a permanent skill.

Ashigaru finishes a task
    ↓
Notices: "I've done this pattern 3 times across different projects"
    ↓
Reports in YAML:  skill_candidate:
                     found: true
                     name: "api-endpoint-scaffold"
                     reason: "Same REST scaffold pattern used in 3 projects"
    ↓
Appears in dashboard.md → You approve → Skill created in .claude/commands/
    ↓
Any agent can now invoke /api-endpoint-scaffold

Skills grow organically from real work — not from a predefined template library. Your skill set becomes a reflection of your workflow.

Quick Start

Windows (WSL2)

cd /mnt/c/tools/multi-agent-shogun
./first_setup.sh

./shutsujin_departure.sh

First-time only: Authentication

After first_setup.sh, run these commands once to authenticate:

# 1. Apply PATH changes
source ~/.bashrc

# 2. OAuth login + Bypass Permissions approval (one command)
claude --dangerously-skip-permissions
#    → Browser opens → Log in with Anthropic account → Return to CLI
#    → "Bypass Permissions" prompt appears → Select "Yes, I accept" (↓ to option 2, Enter)
#    → Type /exit to quit

This saves credentials to ~/.claude/ — you won't need to do it again.

Daily startup

Open an Ubuntu terminal (WSL) and run:

cd /mnt/c/tools/multi-agent-shogun
./shutsujin_departure.sh

📱 Mobile Access — Dedicated Android App (Recommended)

Monitor and command 10 AI agents from your phone with the dedicated Android companion app.

Note: Android only for now. No iOS version — the developer doesn't own an iPhone. If there's demand, please open an Issue. PRs welcome!

Setup

Prerequisites:

• Shogun system running on WSL2 (or Linux server)
• SSH server started (sudo service ssh start)
• Phone and server on same network (LAN or Tailscale)

Steps:

1. Install APK

   1. Download android/release/multi-agent-shogun.apk on your phone (open the file on GitHub → "Download raw file")
   2. Tap the download notification → "Install"
   3. If "Unknown sources" warning appears → "Settings" → enable "Allow from this source" for your browser → go back → "Install"
   4. Done → "Open"

2. Configure SSH (Settings tab)

   Field	Example	Description
   SSH Host	100.xxx.xxx.xxx	Server IP (e.g., Tailscale IP)
   SSH Port	22	Usually 22
   SSH User	your_username	SSH login username
   SSH Key Path	/data/data/.../id_ed25519	Private key path on phone (*1)
   SSH Password	****	Use if no key available
   Project Path	/mnt/c/tools/multi-agent-shogun	Server-side project directory
   Shogun Session	shogun	tmux session name for Shogun
   Agent Session	multiagent	tmux session name for agents

   *1 Transfer your private key to the phone, or use password authentication

3. Save → Switch to Shogun tab → auto-connects

Using Tailscale (connect from anywhere):

# Server-side (WSL2)
curl -fsSL https://tailscale.com/install.sh | sh
sudo tailscaled &
sudo tailscale up --authkey tskey-auth-XXXXXXXXXXXX
sudo service ssh start

Install the Tailscale app on your phone, log in with the same account, and use the displayed Tailscale IP as the SSH Host in the app.

With ntfy notifications:

See ntfy setup section for push notifications from Karo on task completion.

# 1. Clone
git clone https://github.com/yohey-w/multi-agent-shogun.git ~/multi-agent-shogun
cd ~/multi-agent-shogun

# 2. Make scripts executable
chmod +x *.sh

# 3. Run first-time setup
./first_setup.sh

cd ~/multi-agent-shogun
./shutsujin_departure.sh

wsl --install

After Setup

Whichever option you chose, 10 AI agents are automatically launched:

Two tmux sessions are created:

• shogun — connect here to give commands
• multiagent — Karo, Ashigaru, and Gunshi running in the background

How It Works

Step 1: Connect to the Shogun

After running shutsujin_departure.sh, all agents automatically load their instructions and are ready.

Open a new terminal and connect:

tmux attach-session -t shogun

Step 2: Give your first order

The Shogun is already initialized — just give a command:

Research the top 5 JavaScript frameworks and create a comparison table

The Shogun will:

1. Write the task to a YAML file
2. Notify the Karo (manager)
3. Return control to you immediately — no waiting!

Meanwhile, the Karo distributes tasks to Ashigaru workers for parallel execution.

Step 3: Check progress

Open dashboard.md in your editor for a real-time status view:

## In Progress
| Worker | Task | Status |
|--------|------|--------|
| Ashigaru 1 | Research React | Running |
| Ashigaru 2 | Research Vue | Running |
| Ashigaru 3 | Research Angular | Completed |

Project-Unit Operation (Equivalent to Visual Studio "Solution")

Once set up, the Shogun system can handle multiple projects under the same Shogun, switching between them as needed. The unit equivalent to a Visual Studio "solution" is projects/{name}.yaml + context/{name}.md.

1. Running your first project

# (1) Connect to the Shogun (after shutsujin_departure.sh completes)
tmux attach-session -t shogun

# (2) Just give the Shogun your command — the project starts automatically
#     → Shogun writes cmd to queue/shogun_to_karo.yaml and notifies Karo
#     → Karo distributes to Ashigaru for parallel execution
#     → Results aggregate in dashboard.md

No explicit "create a project" command is needed. The Shogun attaches a project: field to the cmd when relevant, and related files are automatically separated.

2. Explicitly registering a project (optional, for long-term work)

For ongoing projects, you can place metadata in projects/{name}.yaml:

# projects/example.yaml
id: example
name: "Sample Project"
working_directory: /path/to/repo
north_star: "The ultimate goal for this project"
notes: |
  Project-specific notes, stakeholders, special rules

The Shogun and Karo reference this file and inject project context when issuing cmds.

Detailed project knowledge (requirements, design, past feedback) lives in context/{name}.md. When the Shogun issues a cmd related to the project, it automatically references this file.

3. Customizing the agent formation

The agent formation (which CLI each agent uses) lives in config/settings.yaml:

cli:
  agents:
    ashigaru1:
      type: codex          # codex / claude / copilot / kimi / opencode / antigravity
      model: gpt-5.5
    ashigaru2:
      type: claude
      model: claude-sonnet-4-6
    # Same for ashigaru3-7, gunshi, karo

OpenCode uses provider-qualified model IDs:

cli:
  agents:
    ashigaru3:
      type: opencode
      model: openrouter/openai/gpt-4o-mini
      variant: high  # optional provider-specific reasoning variant

OpenRouter setup has two separate pieces:

1. Model routing goes in config/settings.yaml as shown above (type: opencode, model: openrouter/...).
2. Provider authentication is configured in OpenCode, not in settings.yaml. Run OpenCode once as the same OS user that will launch Shogun, then use /connect → OpenRouter and paste the API key. OpenCode stores provider credentials in its own user data under that OS user (for example under ~/.local/share/opencode/; the exact file/database is OpenCode-internal). For headless deployments that use environment-based provider credentials, make sure the shell that runs shutsujin_departure.sh has OPENROUTER_API_KEY loaded.

Do not put API keys in config/settings.yaml, config/opencode-tui.json, or .opencode/agents/*.md. Those files only describe routing, tmux-safe keybindings, and generated agent definitions.

When OpenCode is selected, lib/cli_adapter.sh launches it with --agent <agent_id> and the repository-pinned OPENCODE_TUI_CONFIG=config/opencode-tui.json. The TUI command does not accept --variant; if variant: is configured, scripts/build_instructions.sh and scripts/switch_cli.sh synchronize model: / variant: into a git-ignored .opencode/agents/<agent_id>-runtime.md, which OpenCode loads via --agent <agent_id>-runtime.

To switch on the fly, use scripts/switch_cli.sh:

bash scripts/switch_cli.sh ashigaru3 --type claude --model claude-sonnet-4-6
bash scripts/switch_cli.sh ashigaru3 --type opencode --model openrouter/openai/gpt-4o-mini
bash scripts/switch_cli.sh ashigaru3 --type opencode --model openrouter/minimax/minimax-m2.5 --variant xhigh

4. Switching or closing a project

There is no explicit "close project" command. Issuing the next project's cmd automatically switches context.

• Pause temporarily: do nothing. Old cmds remain in queue/ as history, and the Shogun restores state when resumed
• Fully retire: delete projects/{name}.yaml, or add an archived: true flag
• Run in parallel: use the project: field in cmds to keep concurrent projects distinct

5. Carrying experience and settings between projects

What carries forward to future projects:

Memory MCP is the heart of "experience." When you tell the Shogun "don't do X next time" or "remember Y," the Shogun records it in Memory MCP, and all future projects see it.

Detailed flow

You: "Research the top 5 MCP servers and create a comparison table"

The Shogun writes the task to queue/shogun_to_karo.yaml and wakes the Karo. Control returns to you immediately.

The Karo breaks the task into subtasks:

All 5 Ashigaru research simultaneously. You can watch them work in real time:

Results appear in dashboard.md as they complete.

Key Features

⚡ 1. Parallel Execution

One command spawns up to 8 parallel tasks:

You: "Research 5 MCP servers"
→ 5 Ashigaru start researching simultaneously
→ Results in minutes, not hours

🔄 2. Non-Blocking Workflow

The Shogun delegates instantly and returns control to you:

You: Command → Shogun: Delegates → You: Give next command immediately
                                       ↓
                       Workers: Execute in background
                                       ↓
                       Dashboard: Shows results

No waiting for long tasks to finish.

🧠 3. Cross-Session Memory (Memory MCP)

Your AI remembers your preferences:

Session 1: Tell it "I prefer simple approaches"
            → Saved to Memory MCP

Session 2: AI loads memory on startup
            → Stops suggesting complex solutions

📡 4. Event-Driven Communication (Zero Polling)

Agents talk to each other by writing YAML files — like passing notes. No polling loops, no wasted API calls.

Karo wants to wake Ashigaru 3:

Step 1: Write the message          Step 2: Wake the agent up
┌──────────────────────┐           ┌──────────────────────────┐
│ inbox_write.sh       │           │ inbox_watcher.sh         │
│                      │           │                          │
│ Writes full message  │  file     │ Detects file change      │
│ to ashigaru3.yaml    │──change──▶│ (inotifywait, not poll)  │
│ with flock (no race) │           │                          │
└──────────────────────┘           │ Wakes agent via:         │
                                   │  1. Self-watch (skip)    │
                                   │  2. tmux send-keys       │
                                   │     (short nudge only)   │
                                   └──────────────────────────┘

Step 3: Agent reads its own inbox
┌──────────────────────────────────┐
│ Ashigaru 3 reads ashigaru3.yaml  │
│ → Finds unread messages          │
│ → Processes them                 │
│ → Marks as read                  │
└──────────────────────────────────┘

How the wake-up works:

Agent Self-Watch (ASW) Phases — Controls how aggressively the system uses tmux send-keys nudges:

Phase 2 uses the idle flag file (/tmp/shogun_idle_{agent}) to distinguish busy vs idle agents. The Stop hook creates/removes this flag at turn boundaries. This eliminates nudge interruptions during active work while still waking idle agents.

Why can't nudges be fully eliminated? Claude Code's Stop hook only fires at turn end. An idle agent (sitting at the prompt) has no turn ending, so there's no hook to trigger inbox checks. A future Notification hook with idle_prompt blocking support or a periodic timer hook could solve this.

Configure in config/settings.yaml:

asw_phase: 2   # Recommended for Claude Code setups

Or set the default directly in scripts/inbox_watcher.sh (ASW_PHASE variable). Restart inbox_watcher processes after changing.

3-Phase Escalation (v3.2) — If agent doesn't respond:

Key design choices:

• Message content is never sent through tmux — only a short "you have mail" nudge. The agent reads its own file. This eliminates character corruption and transmission hangs.
• Zero CPU while idle — inotifywait blocks on a kernel event (not a poll loop). CPU usage is 0% between messages.
• Guaranteed delivery — If the file write succeeded, the message is there. No lost messages, no retries needed.

📊 5. Agent Status Check

See which agents are busy or idle — instantly, from one command:

# Project mode: full status with task/inbox info
bash scripts/agent_status.sh

# Standalone mode: works with any tmux session
bash scripts/agent_status.sh --session mysession --lang en

Project mode output:

Agent      CLI     Pane      Task ID                                    Status     Inbox
---------- ------- --------- ------------------------------------------ ---------- -----
karo       claude  待機中    ---                                        ---        0
ashigaru1  codex   稼働中    subtask_042a_research                      assigned   0
ashigaru2  codex   待機中    subtask_042b_review                        done       0
gunshi     claude  稼働中    subtask_042c_analysis                      assigned   0

Standalone mode output (no project config needed):

Pane                           State      Agent ID
------------------------------ ---------- ----------
multiagent:agents.0            IDLE       karo
multiagent:agents.1            BUSY       ashigaru1
multiagent:agents.8            BUSY       gunshi

Detection works for Claude Code, Codex CLI, and OpenCode by checking CLI-specific prompt/spinner patterns near the bottom of each tmux pane. The detection logic lives in lib/agent_status.sh — source it in your own scripts:

source lib/agent_status.sh
agent_is_busy_check "multiagent:agents.3" && echo "busy" || echo "idle"

📸 6. Screenshot Integration

VSCode's Claude Code extension lets you paste screenshots to explain issues. This CLI system provides the same capability:

# Set your screenshot folder in config/settings.yaml
screenshot:
  path: "/mnt/c/Users/YourName/Pictures/Screenshots"

# Just tell the Shogun:
You: "Check the latest screenshot"
You: "Look at the last 2 screenshots"
→ AI instantly reads and analyzes your screen captures

Windows tip: Press Win + Shift + S to take screenshots. Set the save path in settings.yaml for seamless integration.

Use cases:

• Explain UI bugs visually
• Show error messages
• Compare before/after states

📁 7. Context Management (4-Layer Architecture)

Efficient knowledge sharing through a four-layer context system:

Persistent Agent Memory (memory/MEMORY.md)

Shogun reads memory/MEMORY.md at every session start. It contains Lord's preferences, lessons learned, and cross-session knowledge — written by Shogun, read by Shogun.

┌─────────────────────────────────────────────────────────────┐
│                    Git Repositories                          │
│                                                              │
│  ┌─────────────────────┐   ┌──────────────────────────┐    │
│  │  multi-agent-shogun │   │      shogun-private        │    │
│  │       (public OSS)  │   │   (your private repo)      │    │
│  │                     │   │                            │    │
│  │ scripts/            │   │ projects/client.yaml  ←──┐ │    │
│  │ instructions/       │   │ context/my-notes.md   ←──┤ │    │
│  │ lib/                │   │ queue/shogun_to_karo.yaml │ │    │
│  │ memory/             │   │ memory/MEMORY.md      ←──┘ │    │
│  │  ├─ MEMORY.md.sample│   │ config/settings.yaml       │    │
│  │  └─ MEMORY.md  ─────┼───┼── same file, tracked here  │    │
│  │     (gitignored)    │   │                            │    │
│  └─────────────────────┘   └──────────────────────────┘    │
│         ↑ anyone can fork        ↑ your data, your repo      │
└─────────────────────────────────────────────────────────────┘

How it works: memory/MEMORY.md lives in the same working directory as the OSS repo, but is excluded from the OSS .gitignore (whitelist-based). You track it in a separate private repo using a bare git repo technique:

# One-time setup (already done by first_setup.sh)
git init --bare ~/.shogun-private.git
alias privategit='git --git-dir=$HOME/.shogun-private.git --work-tree=/path/to/multi-agent-shogun'
privategit remote add origin https://github.com/YOU/shogun-private.git

# Daily use
privategit add -f memory/MEMORY.md projects/my-client.yaml
privategit commit -m "update memory"
privategit push

The OSS .gitignore uses a whitelist approach (default: exclude everything, then explicitly allow OSS files). So private files like memory/MEMORY.md are automatically excluded without needing explicit gitignore entries — just don't add them to the whitelist.

This design enables:

• Any Ashigaru can work on any project
• Context persists across agent switches
• Clear separation of concerns
• Knowledge survives across sessions

/clear Protocol (Cost Optimization)

As agents work, their session context (Layer 4) grows, increasing API costs. /clear wipes session memory and resets costs. Layers 1–3 persist as files, so nothing is lost.

Recovery cost after /clear: ~6,800 tokens (42% improved from v1 — CLAUDE.md YAML conversion + English-only instructions reduced token cost by 70%)

1. CLAUDE.md (auto-loaded) → recognizes itself as part of the Shogun System
2. tmux display-message -t "$TMUX_PANE" -p '#{@agent_id}' → identifies its own number
3. Memory MCP read → restores the Lord's preferences (~700 tokens)
4. Task YAML read → picks up the next assignment (~800 tokens)

The key insight: designing what not to load is what drives cost savings.

Universal Context Template

All projects use the same 7-section template:

This unified format enables:

• Quick onboarding for any agent
• Consistent information management across all projects
• Easy handoff between Ashigaru workers

📱 8. Phone Notifications (ntfy)

Two-way communication between your phone and the Shogun — no SSH, no Tailscale, no server needed.

📱 You (from bed)          🏯 Shogun
    │                          │
    │  "Research React 19"     │
    ├─────────────────────────►│
    │    (ntfy message)        │  → Delegates to Karo → Ashigaru work
    │                          │
    │  "✅ cmd_042 complete"   │
    │◄─────────────────────────┤
    │    (push notification)   │

Setup:

1. Add ntfy_topic: "shogun-yourname" to config/settings.yaml
2. Install the ntfy app on your phone and subscribe to the same topic
3. shutsujin_departure.sh automatically starts the listener — no extra steps

Notification examples:

Free, no account required, no server to maintain. Uses ntfy.sh — an open-source push notification service.

⚠️ Security: Your topic name is your password. Anyone who knows it can read your notifications and send messages to your Shogun. Choose a hard-to-guess name and never share it publicly (e.g., in screenshots, blog posts, or GitHub commits).

Verify it works:

# Send a test notification to your phone
bash scripts/ntfy.sh "Test notification from Shogun 🏯"

If your phone receives the notification, you're all set. If not, check:

• config/settings.yaml has ntfy_topic set (not empty, no extra quotes)
• The ntfy app on your phone is subscribed to the exact same topic name
• Your phone has internet access and ntfy notifications are enabled

Sending commands from your phone:

1. Open the ntfy app on your phone
2. Tap your subscribed topic
3. Type a message (e.g., Research React 19 best practices) and send
4. ntfy_listener.sh receives it, writes to queue/ntfy_inbox.yaml, and wakes the Shogun
5. The Shogun reads the message and processes it through the normal Karo → Ashigaru pipeline

Any text you send becomes a command. Write it like you'd talk to the Shogun — no special syntax needed.

Manual listener start (if not using shutsujin_departure.sh):

# Start the listener in the background
nohup bash scripts/ntfy_listener.sh &>/dev/null &

# Check if it's running
pgrep -f ntfy_listener.sh

# View listener logs (stderr output)
bash scripts/ntfy_listener.sh  # Run in foreground to see logs

The listener automatically reconnects if the connection drops. shutsujin_departure.sh starts it automatically on deployment — you only need manual start if you skipped the deployment script.

Troubleshooting:

Real-world notification screenshots:

Left: Bidirectional phone ↔ Shogun communication · Right: Real-time progress report from Ashigaru

Left: Command completion notification · Right: All 8 Ashigaru completing in parallel

Note: Topic names shown in screenshots are examples. Use your own unique topic name.

SayTask Notifications

Behavioral psychology-driven motivation through your notification feed:

• Streak tracking: Consecutive completion days counted in saytask/streaks.yaml — maintaining streaks leverages loss aversion to sustain momentum
• Eat the Frog 🐸: The hardest task of the day is marked as the "Frog." Completing it triggers a special celebration notification
• Daily progress: 12/12 tasks today — visual completion feedback reinforces the Arbeitslust effect (joy of work-in-progress)

🖼️ 9. Pane Border Task Display

Each tmux pane shows the agent's current task directly on its border:

┌ ashigaru1 Sonnet+T VF requirements ──┬ ashigaru3 Opus+T API research ──────┐
│                                      │                                     │
│  Working on SayTask requirements     │  Researching REST API patterns      │
│                                      │                                     │
├ ashigaru2 Sonnet ───────────────────┼ ashigaru4 Spark DB schema design ───┤
│                                      │                                     │
│  (idle — waiting for assignment)     │  Designing database schema          │
│                                      │                                     │
└──────────────────────────────────────┴─────────────────────────────────────┘

• Working: ashigaru1 Sonnet+T VF requirements — agent name, model (with Thinking indicator), and task summary
• Idle: ashigaru2 Sonnet — model name only, no task
• Display names: Sonnet, Opus, Haiku, Codex, Spark — +T suffix = Extended Thinking enabled
• Updated automatically by the Karo when assigning or completing tasks
• Glance at all 9 panes to instantly know who's doing what

🔊 10. Shout Mode (Battle Cries)

When an Ashigaru completes a task, it shouts a personalized battle cry in the tmux pane — a visual reminder that your army is working hard.

┌ ashigaru1 (Sonnet) ──────────┬ ashigaru2 (Sonnet) ──────────┐
│                               │                               │
│  ⚔️ 足軽1号、先陣切った！     │  🔥 足軽2号、二番槍の意地！   │
│  八刃一志！                   │  八刃一志！                   │
│  ❯                            │  ❯                            │
└───────────────────────────────┴───────────────────────────────┘

How it works:

The Karo writes an echo_message field in each task YAML. After completing all work (report + inbox notification), the Ashigaru runs echo as its final action. The message stays visible above the ❯ prompt.

# In the task YAML (written by Karo)
task:
  task_id: subtask_001
  description: "Create comparison table"
  echo_message: "🔥 足軽1号、先陣を切って参る！八刃一志！"

Shout mode is the default. To disable (saves API tokens on the echo call):

./shutsujin_departure.sh --silent    # No battle cries
./shutsujin_departure.sh             # Default: shout mode (battle cries enabled)

Silent mode sets DISPLAY_MODE=silent as a tmux environment variable. The Karo checks this when writing task YAMLs and omits the echo_message field.

🗣️ SayTask — Task Management for People Who Hate Task Management

What is SayTask?

Task management for people who hate task management. Just speak to your phone.

Talk Coding, not Vibe Coding. Speak your tasks, AI organizes them. No typing, no opening apps, no friction.

• Target audience: People who installed Todoist but stopped opening it after 3 days
• Your enemy isn't other apps — it's doing nothing. The competition is inaction, not another productivity tool
• Zero UI. Zero typing. Zero app-opening. Just talk

"Your enemy isn't other apps — it's doing nothing."

How it Works

1. Install the ntfy app (free, no account needed)
2. Speak to your phone: "dentist tomorrow", "invoice due Friday"
3. AI auto-organizes → morning notification: "here's your day"

 🗣️ "Buy milk, dentist tomorrow, invoice due Friday"
       │
       ▼
 ┌──────────────────┐
 │  ntfy → Shogun   │  AI auto-categorize, parse dates, set priorities
 └────────┬─────────┘
          │
          ▼
 ┌──────────────────┐
 │   tasks.yaml     │  Structured storage (local, never leaves your machine)
 └────────┬─────────┘
          │
          ▼
 📱 Morning notification:
    "Today: 🐸 Invoice due · 🦷 Dentist 3pm · 🛒 Buy milk"

Before / After

Before (v1)	After (v2)
Raw task dump	Clean, organized daily summary

Note: Topic names shown in screenshots are examples. Use your own unique topic name.

Use Cases

• 🛏️ In bed: "Gotta submit the report tomorrow" — captured before you forget, no fumbling for a notebook
• 🚗 While driving: "Don't forget the estimate for client A" — hands-free, eyes on the road
• 💻 Mid-work: "Oh, need to buy milk" — dump it instantly and stay in flow
• 🌅 Wake up: Today's tasks already waiting in your notifications — no app to open, no inbox to check
• 🐸 Eat the Frog: AI picks your hardest task each morning — ignore it or conquer it first

FAQ

Q: How is this different from other task apps? A: You never open an app. Just speak. Zero friction. Most task apps fail because people stop opening them. SayTask removes that step entirely.

Q: Can I use SayTask without the full Shogun system? A: SayTask is a feature of Shogun. Shogun also works as a standalone multi-agent development platform — you get both capabilities in one system.

Q: What's the Frog 🐸? A: Every morning, AI picks your hardest task — the one you'd rather avoid. Tackle it first (the "Eat the Frog" method) or ignore it. Your call.

Q: Is it free? A: Everything is free and open-source. ntfy is free too. No account, no server, no subscription.

Q: Where is my data stored? A: Local YAML files on your machine. Nothing is sent to the cloud. Your tasks never leave your device.

Q: What if I say something vague like "that thing for work"? A: AI does its best to categorize and schedule it. You can always refine later — but the point is capturing the thought before it disappears.

SayTask vs cmd Pipeline

Shogun has two complementary task systems:

…view the full README on GitHub.