🤖 Human-Rent Skill

Name: Human-Rent
Availability: InStock
Rating: 5 (74 reviews)
Author: ZhenStaff

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)

Type	Description	Latency	Cost
`photo_verification`	Take a photo of something	5-15 min	$10-20
`address_verification`	Verify physical address exists	10-20 min	$15-25
`document_scan`	Scan a physical document	10-20 min	$15-25
`visual_inspection`	Detailed visual inspection	15-30 min	$20-40
`voice_verification`	Make a phone call and verify	5-10 min	$10-20
`purchase_verification`	Check product availability	15-30 min	$20-40

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

Task Type	Human Time	Human Cost	Platform Fee (20%)	Total Cost
Quick photo	10 min	$10	$2	$12
Address verify	20 min	$20	$4	$24
Detailed inspect	30 min	$30	$6	$36
Expert consult	60 min	$100	$20	$120

🔧 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)

⚠️ Important Notes

Legal & Ethical

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

Technical

Latency: This is a HIGH-LATENCY tool (minutes to hours)
Cost: Much more expensive than API calls
Availability: Geographic and time-dependent
Reliability: Human workers can fail/cancel tasks
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. 🌍🤖✨

🤖 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)

Type	Description	Latency	Cost
`photo_verification`	Take a photo of something	5-15 min	$10-20
`address_verification`	Verify physical address exists	10-20 min	$15-25
`document_scan`	Scan a physical document	10-20 min	$15-25
`visual_inspection`	Detailed visual inspection	15-30 min	$20-40
`voice_verification`	Make a phone call and verify	5-10 min	$10-20
`purchase_verification`	Check product availability	15-30 min	$20-40

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

Task Type	Human Time	Human Cost	Platform Fee (20%)	Total Cost
Quick photo	10 min	$10	$2	$12
Address verify	20 min	$20	$4	$24
Detailed inspect	30 min	$30	$6	$36
Expert consult	60 min	$100	$20	$120

🔧 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)

⚠️ Important Notes

Legal & Ethical

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

Technical

Latency: This is a HIGH-LATENCY tool (minutes to hours)
Cost: Much more expensive than API calls
Availability: Geographic and time-dependent
Reliability: Human workers can fail/cancel tasks
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. 🌍🤖✨

Human-Rent

🤖 Human-Rent Skill

🎯 Core Concept

📦 Installation

Step 1: Install the Skill

Step 2: Clone & Setup

Step 3: Test

🚀 Usage

When to Use This Skill

💻 Agent Usage Guide

Primary Command: Dispatch Human Task

Check Task Status

List Available Humans

🎨 Task Types

Layer 1: Instant Human (MVP - Currently Available)

Layer 2: Expert on Call (Planned)

Layer 3: Embodied Agent (Planned)

📊 Technical Architecture

Async Function Calling Pattern

MCP Protocol Integration

🎯 Strategic Value for OpenClaw

1. Capability Differentiation

2. Hybrid Intelligence Workflows

3. New Revenue Model

💰 Cost Estimation

🔧 Configuration Options

Task Requirements

Verification Methods

📝 Usage Examples

Example 1: Real Estate Investment

Example 2: Vendor Verification

Example 3: Emergency Response

🛠️ Troubleshooting

Issue 1: No Humans Available

Issue 2: Task Timeout

Issue 3: Low Quality Results

🌟 MVP Features (v0.1.0)

🔮 Roadmap

Phase 2: Automation (Next 3 months)

Phase 3: Scaling (6 months)

Phase 4: Intelligence (12 months)

⚠️ Important Notes

Legal & Ethical

Technical

🎯 Agent Behavior Guidelines

📚 API Reference

dispatchHuman(request)

checkTaskStatus(taskId)

listAvailableHumans(location?, skills?)

📊 Tech Stack

🆕 Version History

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

🚀 Quick Start Example

Download

Skill Info

Human-Rent

🤖 Human-Rent Skill

🎯 Core Concept

📦 Installation

Step 1: Install the Skill

Step 2: Clone & Setup

Step 3: Test

🚀 Usage

When to Use This Skill

💻 Agent Usage Guide

Primary Command: Dispatch Human Task

Check Task Status

List Available Humans

🎨 Task Types

Layer 1: Instant Human (MVP - Currently Available)

Layer 2: Expert on Call (Planned)

Layer 3: Embodied Agent (Planned)

📊 Technical Architecture

Async Function Calling Pattern

MCP Protocol Integration

🎯 Strategic Value for OpenClaw

1. Capability Differentiation

2. Hybrid Intelligence Workflows

3. New Revenue Model

💰 Cost Estimation

`dispatchHuman(request)`

`checkTaskStatus(taskId)`

`listAvailableHumans(location?, skills?)`

`dispatchHuman(request)`

`checkTaskStatus(taskId)`

`listAvailableHumans(location?, skills?)`