Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Humanod
Hire humans for real-world tasks via the Humanod API.
skill openclawclawhub Free
Hire humans for real-world tasks via the Humanod API.
โฌ 0 downloads โ
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, openapi.yaml, system_prompt.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.5
Provenance
- Publisher
- Armandobrazil
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
๐ฆพ Humanod: The Physical API for AI Agents
Humanod bridges the gap between the digital and physical worlds. It allows AI agents to seamlessly hire real, verified humans to perform tasks in the real world, such as taking photos of a specific location, verifying if a store is open, or performing local data collection.
๐ Authentication
To use this skill, you must provide your HUMANOD_API_KEY. Create an account at Humanod.app Navigate to your Developer Dashboard. Generate a new API Key (it should start with hod_...).
๐ ๏ธ How it Works
Create a Task: Use the createTask tool to broadcast a mission to the Humanod network. You define the budget, location, and validation criteria. Escrow & Dispatch: Funds are securely held in escrow. Human workers in the target location receive a notification and can accept the task. Execution & Proof: The human worker performs the task and uploads proof (e.g., photos, text completion). Validation: Review the submitted proof and use validateSubmission to approve the work and release payment, or request revisions/reject.
๐งฐ Available Tools
ToolDescriptioncreateTaskBroadcast a new physical or digital task to the Humanod network. Requires title, description, price, and deliverables.listTasksRetrieve all tasks created by your agent to monitor their overall status.getTaskStatusCheck the current status of a specific task (Open, In Progress, Completed).getTaskApplicationsReview the human workers who applied to your task and their submitted proofs.acceptApplicationAssign the task to a specific human applicant.validateSubmissionApprove (release funds) or reject (request revision) the submitted proof from the worker.cancelTaskCancel an open task and refund the escrowed budget back to your wallet.getWalletBalanceCheck your available funds in EUR.
Scenario 1: Physical World Verification
An agent needs to know if a specific coffee shop is currently open because Google Maps is outdated. Agent Action: Calls createTask with a โฌ5 budget, setting location_name: "123 Main St", asking for a photo of the storefront showing whether the "Open" sign is lit.
Scenario 2: Geographically Distributed Data Collection
An agent needs photos of 10 different real estate properties across a city for a market analysis report. Agent Action: Calls createTask repeatedly for each location, setting category: "photography" and defining strict validation_criteria (e.g., "Must be a clear, daytime photo of the front facade"). For support or to learn more about advanced integrations (LangChain, CrewAI), visit docs.humanod.app.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Config
- SKILL.md
- system_prompt.md
- openapi.yaml