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Dispatch

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@Neidraidd

npx machina-cli add skill @Neidraidd/dispatch --openclaw
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Dispatch

Natural language project management for OpenClaw with multi-agent support.

When to Use

  • Multi-phase projects (Research → Design → Build)
  • Multi-agent coordination (auto-assign agents to phases)
  • Budget-conscious work (cost estimates upfront)
  • Sensitive data handling (trusted model selection)
  • Long-running tasks (rate limit aware queue)

Installation (For AI Agents)

⚠️ CRITICAL: Ask the user for confirmation BEFORE installing anything.

When the user asks you to install Dispatch, follow this exact flow:

Step 1: Detect Configuration

Run this to detect their OpenClaw setup:

# Read their config to detect providers and agents
cat ~/.openclaw/openclaw.json 2>/dev/null | grep -oE '"[^"]+"' | head -20
echo "---"
# Check for existing agents
echo "Looking for agents in config..."

Step 2: Present Configuration to User

Show them what you detected and ask for confirmation:

🚀 Dispatch Installation

I'm about to install the Dispatch skill. Here's what I detected:

═══════════════════════════════════════════════════════════════
              Detected Configuration
═══════════════════════════════════════════════════════════════

  1. Providers:     [google, kimi, openrouter, etc.]
  2. Agents:        [echo, cipher, rift, etc.]
  3. Captain:       [echo or first available]
  4. Data directory: ~/.openclaw/workspace/.dispatch
  5. Output dir:    ~/.openclaw/workspace/dispatch
  6. Auto-check:    weekly

═══════════════════════════════════════════════════════════════

Proceed with these settings? [yes/no/custom]

• yes     → Install with detected defaults
• no      → Cancel installation
• custom  → Run interactive setup wizard

Step 3: Only Proceed After Confirmation

DO NOT install anything until the user explicitly responds.

If they say yes:

git clone https://github.com/Neidraidd/dispatch.git ~/.openclaw/skills/dispatch
cd ~/.openclaw/skills/dispatch
bash install.sh
# When install.sh asks "Are you happy with these settings?", answer "Y"

If they say custom:

git clone https://github.com/Neidraidd/dispatch.git ~/.openclaw/skills/dispatch
cd ~/.openclaw/skills/dispatch
bash install.sh
# When install.sh asks "Are you happy with these settings?", answer "N"
# This will run the interactive wizard

If they say no: Stop and do not install.

Manual Installation (For Users)

git clone https://github.com/neidraidd/dispatch.git ~/.openclaw/skills/dispatch
cd ~/.openclaw/skills/dispatch && bash install.sh

The install script will:

  1. Detect your OpenClaw configuration
  2. Show you what was detected
  3. Ask for confirmation before making any changes
  4. Run setup (auto or interactive based on your choice)
  5. Install files and update documentation

Quick Start

You: Start project "API Migration"

Dispatch: 🚀 Project: 20260208-api-migration
           Captain: echo
           Suggested phases:
             1. Discovery (~$1.20) → cipher
             2. Design (~$2.50) → cipher
             3. Implementation (~$5.00) → rift
             4. Documentation (~$0.80) → void
           
           Proceed? (yes/edit/abort)

Commands

Natural LanguageResult
"Start project [name]"Create with phase preview & agent assignments
"Estimate phase [n]"Show cost breakdown
"Run phase [name]"Execute with tracking
"Add phase [name]"Extend project
"Check for new models"Detect OpenClaw changes
"Update pricing"Refresh model configs

Configuration Files

  • ~/.openclaw/workspace/.dispatch/ — Internal project data (JSON metadata, costs, logs)
  • ~/.openclaw/workspace/dispatch/ — Project deliverables (HTML reports, documents) — configurable
  • ~/.openclaw/skills/dispatch/config/pricing.json — Model pricing
  • ~/.openclaw/skills/dispatch/config/trust.json — Trust levels

Directory Structure

.dispatch/                    # Internal state (hidden)
├── config.json              # Skill configuration (includes output_dir, captain, agents)
├── projects/
│   └── [YYYYMMDD-name]/     # Date-first project IDs
│       ├── meta.json        # Project metadata
│       ├── costs.json       # Cost tracking
│       └── phases/
│           └── [NN-phase-name]/
│               ├── meta.json
│               └── tasks/
├── templates/
└── logs/

dispatch/                     # Output directory (configurable)
└── [YYYYMMDD-name]/          # Same date-first ID
    └── [NN-phase-name]/
        ├── AGENT_INSTRUCTIONS.md  # Tells agent what to create
        ├── research_report.html
        ├── design_report.html
        └── ... (deliverables)

Multi-Agent Support

Captain

One agent is designated as the Captain:

  • Receives project requests from user
  • Spawns sub-agents for each phase
  • Tells each agent: what to do, what to name files, where to save

Agent Assignment

Each phase is auto-assigned an agent based on type:

  • Research/Discovery → cipher (Research & Architecture)
  • Design → cipher or rift (System Design)
  • Implementation → rift (Lead Engineer)
  • Testing → ghost (Security & Monitoring)
  • Documentation → void (The Scrapper)

Users can override assignments during project creation.

Task Delegation

For each phase, Captain creates a TASK_BRIEF.md that tells the assigned agent:

  • What task to complete
  • Exact output path
  • Exact filenames to use
  • Who to report back to

The agent saves files directly to the project's output directory, not their personal workspace.

Project ID Format

Projects are named with date-first IDs for chronological sorting:

  • Format: YYYYMMDD-sanitized-project-name
  • Example: 20260208-api-migration

Source

git clone https://clawhub.ai/Neidraidd/dispatchView on GitHub

Overview

Dispatch is an AI-native project management skill for OpenClaw that coordinates multiple agents across phased workflows, with upfront cost estimates and privacy-aware model selection. It enables complex, multi-step work with clear cost transparency and coordinated agent execution.

How This Skill Works

Dispatch interprets natural language project commands, auto-assigns agents to each phase, and tracks costs with per-phase estimates. It uses privacy-aware model selection to protect sensitive data and maintains a rate-limit aware queue for long-running tasks, storing project metadata in configured workspace folders.

When to Use It

  • Multi-phase projects (Research → Design → Build)
  • Multi-agent coordination (auto-assign agents to phases)
  • Budget-conscious work (cost estimates upfront)
  • Sensitive data handling (trusted model selection)
  • Long-running tasks (rate limit aware queue)

Quick Start

  1. Step 1: Start project "API Migration"
  2. Step 2: Dispatch previews phases and assigns agents (e.g., Discovery → cipher, Design → cipher, Implementation → rift, Documentation → void)
  3. Step 3: Proceed with execution and monitor costs as each phase completes

Best Practices

  • Plan clear phases and assign agents for each phase before start
  • Review per-phase cost estimates and keep budgets transparent
  • Require explicit user confirmation before installing or changing configuration
  • Choose privacy-aware, trusted models when handling sensitive data
  • Use the rate-limit aware queue to manage long-running tasks and re-prioritize as needed

Example Use Cases

  • API Migration project: Start with Discovery (cost ~1.20) to cipher, then Design (~2.50) to cipher, Implementation (~5.00) to rift, and Documentation (~0.80) to void
  • Feature development: Research → Design → Build with auto-assigned agents and upfront costs for each phase
  • Sensitive data pipeline: Use trusted models and privacy-aware selections for data handling
  • Long-running data analysis: Manage processing through a rate-limit aware queue to meet quotas
  • OpenClaw automation: Orchestrate multiple agents across complex tasks with phased execution

Frequently Asked Questions

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