python-skill

# 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 All modules below are **pre-injected as names** in the sandbox. **Do not use `import` statements** — they will fail (`ImportError: __import__ not found`). Reference each name directly. | Name | Reference Style | Description | |------|-----------------|-------------| | `math` | `math.sqrt(2)` | Mathematical functions | | `json` | `json.loads(s)` / `json.dumps(d)` | JSON encoding/decoding | | `datetime` | `datetime.datetime.now()` (the **module**) | Date/time module | | `timedelta` | `timedelta(days=30)` | Duration class (already unwrapped) | | `re` | `re.findall(pat, text)` | Regular expressions | | `random` | `random.randint(1, 10)` | Random number generation | | `Counter` | `Counter(items)` | Count hashable objects (already unwrapped) | | `defaultdict` | `defaultdict(list)` | Dictionary with defaults (already unwrapped) | ### Built-in Variables | Variable | Description | |----------|-------------| | `input_data` | Data from connected workflow nodes (dict) | | `output` | Set this to return a result | ### Output Methods 1. **Set `output` variable**: Returns structured data to the workflow 2. **Use `print()`**: Captured as console output ### Examples **Basic calculation:** ```json { "code": "result = 25 * 4 + 10\nprint(f'Result: {result}')\noutput = result" } ``` **Calculate tip:** ```json { "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:** ```json { "code": "numbers = [random.randint(1, 100) for _ in range(5)]\nprint(f'Random numbers: {numbers}')\noutput = numbers" } ``` **Date calculations:** ```json { "code": "today = datetime.datetime.now()\nfuture = today + timedelta(days=30)\nresult = f'{future.year:04d}-{future.month:02d}-{future.day:02d}'\nprint(f'30 days from now: {result}')\noutput = result" } ``` **Process data:** ```json { "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:** ```json { "code": "json_str = '{\"name\": \"John\", \"age\": 30}'\ndata = json.loads(json_str)\nprint(f'Name: {data[\"name\"]}')\noutput = data" } ``` **Count items:** ```json { "code": "items = ['apple', 'banana', 'apple', 'cherry', 'banana', 'apple']\ncounts = dict(Counter(items))\nprint(f'Counts: {counts}')\noutput = counts" } ``` **Text processing:** ```json { "code": "text = '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:** ```json { "success": true, "result": {"tip": 12.825, "total": 98.325}, "output": "Tip: $12.83\nTotal: $98.33" } ``` **Error:** ```json { "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 1. **Always set `output`**: This returns data to the workflow 2. **Use `print()` for debugging**: Output is captured and returned 3. **Keep code focused**: One task per execution Some datetime formatting helpers such as `strftime` perform internal imports and can fail in this restricted namespace. Use direct fields or simple formatting, as in the date example. 4. **Handle expected invalid input**: Prefer explicit guards. Named exception classes are not injected into this executor's restricted builtins; the runtime reports uncaught failures with line information. 5. **No network access**: Use http-skill for web requests 6. **No file system access**: Restricted to safe operations 7. **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 1. Connect the **Python Executor** node to Zeenie's `input-tools` handle 2. Python must be installed on the server