n8n-code-python_skill

Overview

This skill helps you write Python code inside n8n Code nodes, with practical templates, data access patterns, and error-prevention rules. It focuses on the Python (Beta) and Python (Native) modes, explains standard-library availability, and highlights critical return and webhook behaviors. Use it to produce reliable, n8n-compatible Python snippets and patterns for real workflows.

How this skill works

It inspects common n8n Code node requirements and translates them into concise Python patterns: how to read inputs (_input/_items/_json/_node), how to return data in the required list-of-{'json': ...} format, and which standard library modules are available. It also flags what will break (external imports) and provides mode-specific guidance for Python (Beta) helpers versus Python (Native) pure-Python usage.

When to use it

• When you prefer Python syntax for data transformations or string/regex processing.
• When you need standard library features (datetime, re, hashlib, urllib.parse, statistics).
• When JavaScript helpers are not required and you want simpler Python logic.
• When you must process or aggregate multiple items in one execution using _input.all().
• When you need clear examples for webhook body access and safe dict handling.

Best practices

• Prefer JavaScript for most cases; use Python only when its standard library or developer comfort matters.
• Always return a list of dicts with a 'json' key (or an empty list) to avoid execution failures.
• Use _input.all(), _input.first(), or _input.item according to execution mode; use .get() to avoid KeyError.
• Avoid external libraries; rely on HTTP Request node or JavaScript for network calls.
• Handle None and missing fields explicitly and use list comprehensions for concise transformations.

Example use cases

• Batch-aggregate amounts from multiple items and return totals and averages with _input.all().
• Extract and deduplicate emails from text using re and return a single summary object.
• Validate incoming webhook payloads (access under _json['body']) and annotate errors per item.
• Compute simple statistics (mean, median, stdev) using the statistics module for numeric lists.
• Combine outputs from two nodes via _node['NodeName']['json'] and return a merged result.