What makes Python Flow Engine notable?

1K downloads. Trust evidence available

How should Python Flow Engine be evaluated before use?

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Python Flow Engine

Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation tha... Skill: Python Flow Engine Owner: dnaxxx-hub Summary: Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation tha... Tags: flow:1.0.0, latest:1.0.0, orchestration:1.0.0, pipeline:1.0.0, python:1.0.0, workflow:1.0.0 Version history: v0.1.0 | 2026-05-28T22:27:30.962Z | user Initial: lightweight Python flow orchestration lib

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

0.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 0.1.0release · observed May 28, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s171sre9mpk2gy28m6evctkt5s83n7ss:python-flow-engine

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-dnaxxx-hub-python-flow-engine/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

10,210 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: python-flow-engine
description: "Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation that needs serial/parallel/conditional execution. Triggers: pipeline, flow, orchestrate, workflow, ETL, data pipeline, process chain."
---

# Python Flow Engine

Lightweight Python flow orchestration — no external dependencies. Build pipelines with serial (>>), parallel (//), and conditional (|) node execution.

## Core API

### Node

```python
from pipeline import Node

def my_fn(context, data):
    # context: shared dict for the pipeline run
    # data: input from previous node
    return modified_data

node = Node(my_fn, name="step1", timeout=10, retry=1)
```

### Pipeline

```python
pipe = Pipeline()
pipe.add_node(node)
pipe.run(start_node, input_data)
```

### Connection Operators

| Operator | Mode | Behavior |
|----------|------|----------|
| `a >> b` | Serial | a runs, then b runs with a's output |
| `a // b` | Parallel | a and b run concurrently (bidirectional) |
| `a \| b` | Conditional | b runs based on a's output (use `connect`) |

For conditional connections, use `connect()`:

```python
pipe.connect(node_a, node_b, "conditional",
    condition=lambda ctx, data: ctx.get("is_valid", False))
```

Or use `Node.condition` parameter:

```python
node_b = Node(fn, condition=lambda ctx, data: data > 0)
```

### Visualization

```python
print(pipe.visualize())  # outputs Mermaid.js flowchart
```

### Error Handling

- **retry**: `Node(fn, retry=2)` retries up to 3 total attempts
- **timeout**: `Node(fn, timeout=5)` raises `RuntimeError` if execution exceeds 5s

## Available Scripts

- `scripts/pipeline.py` — Core library (importable, no dependencies)
- `scripts/pipeline_demo.py` — End-to-end demo (6 scenarios)

## Quick Start

```python
from pipeline import Node, Pipeline

def double(ctx, x): return x * 2
def add_one(ctx, x): return x + 1

pipe = Pipeline()
n1 = Node(double, name="double")
n2 = Node(add_one, name="add_one")
pipe.add_node(n1)
pipe.add_node(n2)
n1 >> n2

result = pipe.run(n1, 5)  # 5*2+1 = 11
print(result)  # 11
print(pipe.visualize())
```

Run the full demo:
```
python scripts/pipeline_demo.py
```

_meta.json

{
  "ownerId": "kn71mebbt22z0hvy3y0s8j50s183m8ba",
  "slug": "python-flow-engine",
  "version": "0.1.0",
  "publishedAt": 1780007250962
}

skill-card.md

## Description: <br>
Lightweight Python flow orchestration library for building data processing pipelines, workflow automation, ETL tasks, and multi-step computations with serial, parallel, and conditional execution. <br>

This skill is ready for commercial/non-commercial use. <br>

## Publisher: <br>
[dnaxxx-hub](https://clawhub.ai/user/dnaxxx-hub) <br>

### License/Terms of Use: <br>
MIT-0 <br>


## Use Case: <br>
Developers and engineers use this skill to build or explain lightweight Python pipelines that coordinate callable steps with serial, parallel, conditional, retry, timeout, and Mermaid visualization support. <br>

### Deployment Geography for Use: <br>
Global <br>

## Known Risks and Mitigations: <br>
Risk: The trigger words are generic, so the skill may appear for broad pipeline or workflow requests where a more specific skill is a better fit. <br>
Mitigation: Confirm the user wants lightweight Python orchestration guidance before applying this skill's examples or library code. <br>
Risk: Pipeline nodes execute user-provided Python callables, so unsafe callables can still perform unintended local actions. <br>
Mitigation: Review node functions before execution and run untrusted pipeline code in an appropriate isolated environment. <br>


## Reference(s): <br>


## Skill Output: <br>
**Output Type(s):** [text, markdown, code, shell commands, guidance] <br>
**Output Format:** [Markdown with Python code examples and shell commands] <br>
**Output Parameters:** [1D] <br>
**Other Properties Related to Output:** [May include Mermaid flowchart text generated by the pipeline visualization helper.] <br>

## Skill Version(s): <br>
0.1.0 (source: server release evidence) <br>

## Ethical Considerations: <br>
Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>

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