A zero-dependency Python library for constructing data pipelines with serial, parallel, and conditional execution paths.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install python-flow-engine
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 python-flow-engine 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 Python Flow Engine?
Python Flow Engine is a lightweight Python workflow orchestration library designed for zero external dependencies. It enables developers to structure complex computations, workflow automations, and data processing tasks into clean, modular pipelines. By leveraging clean operators, you can seamlessly tie custom logic execution steps together to build reliable processing networks.
Integrating this tool within your Openclaw Skills setup gives AI agents a reliable framework to handle multi-step computational logic cleanly. Whether managing intricate AI workflows or executing systematic text, data, or file pipelines, this engine delivers flexible, native Python execution controls with integrated error handling.
Python Flow Engine Use Cases
- Orchestrating multi-step computations and data pipelines inside Openclaw Skills automation frameworks.
- Constructing lightweight ETL processes without installing bulky workflow managers.
- Building concurrent or parallel processing tasks natively using execution operators.
- Implementing step-by-step agentic execution flows with built-in retry and timeout guards.
How Python Flow Engine Works
- Define Nodes: Wrap targeted processing logic into Node instances, specifying execution boundaries like timeouts and retries.
- Establish Relationships: Group multiple steps into a cohesive Pipeline using expressive visual operators or manual conditional logic connections.
- Execute Pipeline: Trigger the runner from a designated root step, optionally passing contextual variables or data payloads downstream.
- Handle Execution and Failures: Rely on built-in timeout monitors and retry counts to recover gracefully when transient exceptions or long-running tasks occur.
- Generate Visual Reports: Export the finalized layout structure cleanly to Mermaid.js strings for live status mapping and visual documentation.
Python Flow Engine Setup
To deploy Python Flow Engine within your Openclaw Skills workflow environment, copy the engine script into your Python workspace:
# Download or place the core engine files
mkdir -p scripts
cp /path/to/source/pipeline.py scripts/pipeline.py
# Run the verification demo suite
python scripts/pipeline_demo.py
Python Flow Engine Data Schema & Taxonomy
The Python Flow Engine organizes and tracks operational state utilizing native structural models and state-sharing mechanisms:
| Data Component | Data Type | Primary Role |
|---|---|---|
context |
dict |
Global key-value dictionary shared across every node execution step in a specific run. |
data |
Any |
Input data payload accepted from preceding nodes, allowing chaining modifications downstream. |
Node.name |
str |
Unique tracking identification label mapped within the overall pipeline layout topology. |
Mermaid.js Output |
str |
Generated flowchart serialization string highlighting structural connectivity maps. |
Python Flow Engine Advanced Features
- Expressive chaining syntax utilizing operator overloads (
>>for serial,//for parallel,|for conditional execution mapping). - Built-in failure mitigation supporting specified retry attempts on individual computation boundaries.
- Custom function hooks or lambdas evaluation passed directly into node processing checkpoints dynamically.
- Automated extraction tool outputting comprehensive Mermaid.js graph code natively to simplify structural UI documentation.
SKILL.md
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