A powerful orchestration engine for building, versioning, and executing multi-step AI model pipelines.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install eachlabs-workflows
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install eachlabs-workflows using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is EachLabs Workflows?
EachLabs Workflows provide a sophisticated framework for developers to chain diverse AI models into cohesive, automated processes. By leveraging Openclaw Skills, users can bridge the gap between isolated model outputs, passing data seamlessly from one step to the next using a flexible variable syntax. This skill is designed for those who need more than just a single prompt; it enables the construction of entire production pipelines that can transform a simple text input into a polished, high-resolution video or complex data structure.
The system utilizes a two-phase approach to workflow management, separating the workflow container from its specific versioned logic. This architecture ensures that Openclaw Skills users can iterate on their AI logic, test new model versions, and maintain production stability through robust version control and schema validation.
EachLabs Workflows Use Cases
- Chaining image generation with video synthesis for automated marketing and social media content.
- Building complex content transformation pipelines that include text-to-image, editing, and upscaling steps.
- Automating bulk media processing for e-commerce by applying consistent AI enhancements to large product catalogs.
- Creating version-controlled AI workflows that allow for safe testing and rapid deployment of new model configurations.
How EachLabs Workflows Works
- Initialize a workflow container by defining its name and purpose via the API.
- Create a specific version for the workflow, defining an array of steps that include model slugs and version requirements.
- Map inputs and outputs using the templating syntax, such as using step_name.output to feed the next model in the chain.
- Trigger the workflow execution with dynamic variables provided in the execution request.
- Monitor the process through execution IDs using either active polling or asynchronous webhook notifications.
EachLabs Workflows Setup
To start using EachLabs Workflows with Openclaw Skills, follow these steps:
- Sign up and obtain your API key at eachlabs.ai.
- Set your environment variable to authenticate your requests:
export EACHLABS_API_KEY='your_api_key_here'
- Verify your setup by retrieving your list of active workflows:
curl https://workflows.eachlabs.run/api/v1/workflows -H "X-API-Key: $EACHLABS_API_KEY"
EachLabs Workflows Data Schema & Taxonomy
The skill organizes workflow data through a hierarchical structure of containers, versions, and executions. Openclaw Skills users should adhere to the following data mapping:
| Object | Description | Key Reference |
|---|---|---|
| Workflow | The top-level container for a specific AI process | workflowID |
| Version | A specific set of steps and model configurations | versionID |
| Step | An individual model execution within a version | name |
| Input Variable | Dynamic data passed during a trigger | {{inputs.key}} |
| Step Reference | Passing output from one model to the next | {{steps.name.output}} |
EachLabs Workflows Advanced Features
- Multi-step chaining support allowing for infinite horizontal scaling of AI processes.
- Bulk execution capabilities to process multiple input sets simultaneously with a single API call.
- Native webhook integration for building event-driven architectures and real-time notifications.
- Granular versioning that enables rollbacks and A/B testing of different model pipelines.
- Automated model schema validation to ensure input parameters match the requirements of selected AI models.
SKILL.md
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