tomevault-io Python Code Execution Tool
Execute Python code for calculations, data processing, and automation tasks.
How It Works
This skill provides instructions for the Python Executor tool node. Connect the Python Executor node to Zeenie's input-tools handle to enable Python code execution.
python_code Tool
Execute Python code and return results.
Schema Fields
| Field | Type | Required | Description |
|---|---|---|---|
| code | string | Yes | Python code to execute |
Available Libraries
| Library | Description |
|---|---|
math |
Mathematical functions (sqrt, pow, sin, cos, etc.) |
json |
JSON encoding/decoding |
datetime |
Date and time manipulation |
timedelta |
Time duration calculations |
re |
Regular expressions |
random |
Random number generation |
Counter |
Count hashable objects |
defaultdict |
Dictionary with default values |
Built-in Variables
| Variable | Description |
|---|---|
input_data |
Data from connected workflow nodes (dict) |
output |
Set this to return a result |
Output Methods
- Set
outputvariable: Returns structured data to the workflow - Use
print(): Captured as console output
Examples
Basic calculation:
{
"code": "result = 25 * 4 + 10\nprint(f'Result: {result}')\noutput = result"
}
Calculate tip:
{
"code": "bill = 85.50\ntip_percent = 15\ntip = bill * (tip_percent / 100)\ntotal = bill + tip\nprint(f'Tip: ${tip:.2f}')\nprint(f'Total: ${total:.2f}')\noutput = {'tip': tip, 'total': total}"
}
Generate random numbers:
{
"code": "import random\nnumbers = [random.randint(1, 100) for _ in range(5)]\nprint(f'Random numbers: {numbers}')\noutput = numbers"
}
Date calculations:
{
"code": "from datetime import datetime, timedelta\ntoday = datetime.now()\nfuture = today + timedelta(days=30)\nresult = future.strftime('%Y-%m-%d')\nprint(f'30 days from now: {result}')\noutput = result"
}
Process data:
{
"code": "data = input_data.get('numbers', [1, 2, 3, 4, 5])\ntotal = sum(data)\naverage = total / len(data)\nprint(f'Total: {total}, Average: {average}')\noutput = {'total': total, 'average': average}"
}
Parse JSON:
{
"code": "import json\njson_str = '{\"name\": \"John\", \"age\": 30}'\ndata = json.loads(json_str)\nprint(f'Name: {data[\"name\"]}')\noutput = data"
}
Count items:
{
"code": "from collections import Counter\nitems = ['apple', 'banana', 'apple', 'cherry', 'banana', 'apple']\ncounts = dict(Counter(items))\nprint(f'Counts: {counts}')\noutput = counts"
}
Text processing:
{
"code": "import re\ntext = 'Contact: john@example.com or jane@test.org'\nemails = re.findall(r'\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b', text)\nprint(f'Found emails: {emails}')\noutput = emails"
}
Response Format
Success:
{
"success": true,
"result": {"tip": 12.825, "total": 98.325},
"output": "Tip: $12.83\nTotal: $98.33"
}
Error:
{
"error": "name 'undefined_var' is not defined"
}
Use Cases
| Use Case | Approach |
|---|---|
| Math calculations | Use math library functions |
| Date/time operations | Use datetime and timedelta |
| Data analysis | Use list comprehensions, sum, len |
| Random generation | Use random library |
| Text parsing | Use re regular expressions |
| JSON manipulation | Use json.loads() and json.dumps() |
| Counting | Use Counter from collections |
Guidelines
- Always set
output: This returns data to the workflow - Use
print()for debugging: Output is captured and returned - Keep code focused: One task per execution
- Handle errors: Use try/except for robust code
- No network access: Use http-skill for web requests
- No file system access: Restricted to safe operations
- Timeout: Default 30 seconds max execution time
Security Restrictions
- No network/socket operations
- No file system access outside designated areas
- No subprocess/shell commands
- Limited execution time (30 seconds)
- Sandboxed environment
Setup Requirements
- Connect the Python Executor node to Zeenie's
input-toolshandle - Python must be installed on the server
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