Human Rent

🤖 Human-Rent Skill

The world's first "Human-as-a-Service" platform for AI Agents

Enable OpenClaw agents to dispatch real human workers for physical world tasks that AI cannot perform: take photos, verify addresses, inspect equipment, make phone calls, and more.

🎯 Core Concept

Transform AI agents from "digital-only" to "hybrid intelligence" by giving them the ability to interact with the physical world through verified human workers.

AI Agent → Human-Rent Skill → Human Worker → Physical Task → Verified Result → AI Agent

📦 Installation

Step 1: Install the Skill

clawhub install human-rent

Step 2: Clone & Setup

# Clone repository
git clone https://github.com/ZhenRobotics/openclaw-human-rent.git ~/openclaw-human-rent
cd ~/openclaw-human-rent

# Install dependencies
npm install

# Verify installation
./agents/human-rent-cli.sh help

Step 3: Test

# Run test dispatch
./agents/human-rent-cli.sh test

🚀 Usage

When to Use This Skill

AUTO-TRIGGER when user's request involves:

• Physical verification: "Check if this address exists", "Verify this business is open"
• Photo evidence: "Take a photo of X", "Get visual proof of Y"
• Real-world inspection: "Inspect this equipment", "Check product availability"
• Human interaction: "Call this number and verify", "Ask the receptionist"
• Trust verification: "Is this company real?", "Does this person actually exist?"

TRIGGER EXAMPLES:

• "I need someone to verify this address in San Francisco"
• "Take a photo of the building at 123 Main Street"
• "Check if this store has the product in stock"
• "Call this number and confirm the appointment"
• "Inspect this equipment and report its condition"

DO NOT USE when:

• Task can be done with digital APIs (use web search, image API, etc.)
• No physical presence required
• Task is illegal or dangerous

💻 Agent Usage Guide

Primary Command: Dispatch Human Task

When user requests physical-world action, execute:

# Method 1: CLI (Recommended)
~/openclaw-human-rent/agents/human-rent-cli.sh dispatch "instruction"

# Method 2: Direct agent
cd ~/openclaw-human-rent && npx tsx agents/dispatch-task.ts "instruction"

# Method 3: With location
~/openclaw-human-rent/agents/human-rent-cli.sh dispatch "instruction" --location="37.7749,-122.4194"

Example:

User says: "I need someone to verify the address 123 Market St in SF exists"

Execute:

~/openclaw-human-rent/agents/human-rent-cli.sh dispatch "Go to 123 Market Street, San Francisco and take a photo of the building entrance to verify it exists" --location="37.7749,-122.4194"

Check Task Status

# Check status by task ID
~/openclaw-human-rent/agents/human-rent-cli.sh status <task_id>

List Available Humans

# See available human workers
~/openclaw-human-rent/agents/human-rent-cli.sh humans

🎨 Task Types

Layer 1: Instant Human (MVP - Currently Available)

Layer 2: Expert on Call (Planned)

• Legal document review
• Medical image analysis
• Code audit
• Professional consultation

Layer 3: Embodied Agent (Planned)

• Attend meetings
• Equipment installation
• Long-term physical monitoring

📊 Technical Architecture

Async Function Calling Pattern

// Agent calls human-rent skill
const task = await openclaw.skills.human_rent.dispatch({
  task_type: "photo_verification",
  location: "37.7749,-122.4194",
  instruction: "Take photo of building entrance",
  budget: "$15",
  timeout: "30min"
});

// Returns immediately with task ID
console.log(task.task_id); // "abc-123-def"
console.log(task.status); // "assigned"

// Agent continues other work (non-blocking)
await openclaw.doOtherStuff();

// Later, check status
const result = await openclaw.skills.human_rent.checkStatus(task.task_id);
if (result.status === "completed") {
  // Process human's result
  console.log(result.result.photos); // ["https://..."]
  console.log(result.result.notes); // Human's observations
}

MCP Protocol Integration

This skill implements Model Context Protocol (MCP) extensions:

{
  "name": "human-rent",
  "type": "physical_resource",
  "latency": "high",
  "cost_model": "per_task",
  "capabilities": [
    "visual_verification",
    "physical_manipulation",
    "social_interaction"
  ]
}

🎯 Strategic Value for OpenClaw

1. Capability Differentiation

Problem: All AI agents are limited to digital information Solution: OpenClaw can verify physical reality

Example Use Cases:

• Due Diligence: Investor agent verifies company office exists before investment
• E-commerce: Purchasing agent inspects warehouse before bulk order
• Security: Safety agent verifies suspicious package before opening

2. Hybrid Intelligence Workflows

Enable "Human-in-the-Loop" automation:

Step 1: AI analysis (confidence: 85%)
Step 2: Human verification (if confidence < 90%)
Step 3: AI decision (based on verified data)

This makes OpenClaw agents auditable and trustworthy for regulated industries (finance, healthcare, legal).

3. New Revenue Model

• Per-task fee: $15-50/task
• Platform fee: 20% commission
• Subscription: $99/month for unlimited tasks

Potential: If 10K agents use 1 task/day at $15 → $1M daily revenue (20% = $200K to platform)

💰 Cost Estimation

🔧 Configuration Options

Task Requirements

requirements: {
  minHumanRating: 4.5,  // Require highly rated workers
  requiredSkills: ['photography', 'legal_reading'],
  requiredEquipment: ['smartphone', 'tape_measure'],
  languageRequired: ['en', 'zh'],
  certificationRequired: ['driver_license']
}

Verification Methods

• automatic: AI-based verification (fast, cheap)
• cross_check: Multiple humans verify same task (slower, more reliable)
• manual_review: Platform expert reviews (slowest, highest quality)
• none: Trust human worker (fastest, lowest cost)

📝 Usage Examples

Example 1: Real Estate Investment

Scenario: AI agent analyzing potential property investment

// Agent is uncertain about property condition
const task = await dispatch({
  task_type: "visual_inspection",
  location: "37.7749,-122.4194",
  instruction: "Inspect the property at 123 Main St. Check for: roof condition, foundation cracks, water damage, neighborhood safety. Take 10+ photos.",
  budget: "$50",
  timeout: "60min",
  requirements: {
    requiredSkills: ['property_inspection'],
    minHumanRating: 4.5
  }
});

// Agent continues analysis while waiting
await analyzeFinancials();
await checkLegalRecords();

// Retrieve human's findings
const result = await checkStatus(task.task_id);
// Use real-world data for final decision

Example 2: Vendor Verification

Scenario: Procurement agent vetting new supplier

human-rent dispatch "Visit supplier's warehouse at 456 Industrial Rd. Verify: business license displayed, clean facilities, proper safety equipment, actual inventory matches claim. Interview manager if possible."

Example 3: Emergency Response

Scenario: Security agent receives suspicious package alert

human-rent dispatch "URGENT: Suspicious package at office entrance. DO NOT TOUCH. Call building security (415-555-0123), evacuate area, wait for authorities. Take photos from safe distance." --priority=urgent --budget="$100"

🛠️ Troubleshooting

Issue 1: No Humans Available

Error: "No suitable humans found for this task"

Solutions:

• Expand search radius (default: 5km)
• Increase budget to attract workers
• Reduce requirements (skills, rating, etc.)
• Try different time of day

Issue 2: Task Timeout

Error: "Task timed out"

Solutions:

• Increase timeout (default: 30min)
• Check if location is accessible
• Verify task is clear and reasonable
• Increase budget for complex tasks

Issue 3: Low Quality Results

Solutions:

• Require higher human rating (4.5+)
• Use cross-check verification
• Provide detailed instructions
• Require specific equipment

🌟 MVP Features (v0.1.0)

• ✅ Async task dispatch system
• ✅ Geographic matching (5 mock humans in SF)
• ✅ 6 task types supported
• ✅ Task status tracking
• ✅ Simulated human completion (for testing)
• ✅ MCP protocol interface
• ✅ CLI tools
• ✅ TypeScript type safety

🔮 Roadmap

Phase 2: Automation (Next 3 months)

• Real geographic matching algorithm
• Stripe payment integration
• Webhook callbacks for async updates
• Cross-check verification
• Human mobile app (for workers)

Phase 3: Scaling (6 months)

• Multi-city support (NY, LA, Beijing, London)
• Expert-on-call marketplace
• Blockchain result verification
• AR glasses for workers
• Integration with Uber/TaskRabbit APIs

Phase 4: Intelligence (12 months)

• AI task routing optimization
• Predictive human availability
• Automated quality scoring
• Natural language task parsing
• Multi-human collaboration

⚠️ Important Notes

Legal & Ethical

1. Liability: Human workers assume responsibility for their actions (contractor model)
2. Privacy: No PII collection without consent
3. Safety: Dangerous tasks are rejected automatically
4. Labor Law: Compliant with gig economy regulations
5. Geographic: Initially US-only (expand after legal review)

Technical

1. Latency: This is a HIGH-LATENCY tool (minutes to hours)
2. Cost: Much more expensive than API calls
3. Availability: Geographic and time-dependent
4. Reliability: Human workers can fail/cancel tasks
5. MVP Mode: Currently using mock data for testing

🎯 Agent Behavior Guidelines

When using this skill, agents should:

DO:

• ✅ Use for tasks that REQUIRE physical presence
• ✅ Provide clear, specific instructions
• ✅ Set appropriate budgets (humans value their time)
• ✅ Handle async results (don't block waiting)
• ✅ Verify results before making decisions
• ✅ Respect human workers (polite instructions)

DON'T:

• ❌ Use for tasks that can be done digitally
• ❌ Request illegal or dangerous actions
• ❌ Expect instant results
• ❌ Underpay workers
• ❌ Share sensitive/private information unnecessarily
• ❌ Abuse the service with spam tasks

📚 API Reference

dispatchHuman(request)

Dispatch a task to a human worker.

Parameters:

{
  task_type: string,        // Type of task
  instruction: string,      // Clear instructions for human
  location?: string,        // "lat,lng" format
  budget?: string,          // "$15" format
  timeout?: string,         // "30min" format
  priority?: string,        // low|normal|high|urgent
  requirements?: object     // Skills, rating, equipment
}

Returns:

{
  task_id: string,
  status: "assigned" | "failed",
  estimated_completion?: string,
  message: string
}

checkTaskStatus(taskId)

Check status of a dispatched task.

Returns:

{
  task_id: string,
  status: TaskStatus,
  result?: TaskResult,
  verification?: Verification,
  message: string
}

listAvailableHumans(location?, skills?)

Get available human workers.

Returns:

{
  statistics: { total, available, averageRating },
  available_humans: Human[]
}

📊 Tech Stack

• TypeScript: Type-safe development
• Node.js: Runtime environment
• MCP: Model Context Protocol
• Express: API server (planned)
• Stripe: Payment processing (planned)
• Blockchain: Result verification (planned)

🆕 Version History

v0.1.0 - MVP Release (2026-03-07)

• ✨ Initial release with core functionality
• 🤖 Async task dispatch system
• 👥 Mock human pool (5 workers in SF)
• 📊 6 task types supported
• 🔧 CLI tools
• 📡 MCP protocol interface
• 🧪 Full testing simulation

Project Status: 🧪 MVP - Testing Phase

License: MIT

Author: @ZhenStaff

Support: https://github.com/ZhenRobotics/openclaw-human-rent/issues

ClawHub: https://clawhub.ai/ZhenStaff/human-rent

🚀 Quick Start Example

# 1. Install
git clone https://github.com/ZhenRobotics/openclaw-human-rent.git ~/openclaw-human-rent
cd ~/openclaw-human-rent && npm install

# 2. Test
./agents/human-rent-cli.sh test

# 3. Dispatch real task
./agents/human-rent-cli.sh dispatch "Take a photo of the Golden Gate Bridge"

# 4. Check status
./agents/human-rent-cli.sh status <task_id>

# 5. List humans
./agents/human-rent-cli.sh humans

Make AI agents that can touch the physical world. 🌍🤖✨