A powerful orchestration skill for chaining AI agent tasks into sequential, multi-step processing pipelines.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pilot-task-chain
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 pilot-task-chain 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 pilot-task-chain?
The pilot-task-chain skill provides a robust framework for building sequential workflows where the output of one AI agent becomes the input for the next. This capability allows developers to decompose complex, monolithic tasks into a series of smaller, manageable steps handled by specialized agents. By leveraging the pilot-protocol and the pilotctl command-line utility, it bridges the gap between individual agent actions and sophisticated end-to-end automation.
At its core, this skill is designed for high-interdependency environments within the Openclaw Skills ecosystem. It excels at handling data transformation, multi-stage research, and complex deployment scenarios where data must flow seamlessly through a predefined logical path. By utilizing this skill, users can ensure that intermediate results are accurately routed and processed, providing a level of control and reliability that single-agent systems often lack.
pilot-task-chain Use Cases
- Creating multi-step workflows where each stage relies on the validated output of a previous step.
- Routing data through a series of specialized agents (e.g., a researcher, a synthesizer, and an editor).
- Automating complex DevOps or data engineering pipelines that require sequential logic.
- Composing high-level agent behaviors by chaining together several simple Openclaw Skills.
How pilot-task-chain Works
- The user initiates the sequence by submitting the first task to a designated agent via pilotctl.
- The system monitors the task list to track the progress and status of the submitted task.
- Upon task completion, the skill extracts the result payload, typically using a tool like jq to parse the JSON output.
- This extracted result is then injected into the task description for the subsequent agent in the pipeline.
- The process repeats until the final agent in the chain completes the ultimate objective.
pilot-task-chain Setup
To get started with this skill, you must have the pilot-protocol installed and the pilotctl binary present on your system PATH. The pilot daemon must be active.
# Start the pilot daemon if not already running
pilotctl daemon start
# Ensure jq is installed for result parsing
# On macOS/Linux:
sudo apt-get install jq # or brew install jq
Configure your environment variables for the agents you intend to use in your pipeline (e.g., AGENT_1, AGENT_2).
pilot-task-chain Data Schema & Taxonomy
The skill relies on JSON-formatted task metadata managed by the pilot-protocol. The following table describes the primary data structure used during task chaining:
| Attribute | Type | Description |
|---|---|---|
| task_id | String | The unique identifier for a specific step in the chain. |
| status | String | Current state (submitted, running, completed, or failed). |
| result | String/JSON | The actual data output generated by the agent. |
| agent_id | String | The identifier of the agent performing the specific task step. |
pilot-task-chain Advanced Features
- Scripted polling loops for automated state management and error handling.
- Dynamic task generation based on intermediate JSON responses.
- Multi-agent orchestration across diverse specialized Openclaw Skills.
- Integration with bash for high-level logic and conditional branching between pipeline steps.
SKILL.md
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