Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tmolavi/mcp-agent-skills-hub --skill n8n-code-pythongit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/n8n-code-python)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/n8n-code-python"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/n8n-code-python/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/n8n-code-python"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/n8n-code-python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.04557 |
| Opus 5 | $0.00026 | $0.02278 |
| Sonnet 5 | $0.00010 | $0.00911 |
| Haiku 4.5 | $0.00005 | $0.00456 |
Grade A, and why
n8n-code-python scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
6. **Standard library only**: json, datetime, re, base64, hashlib, urllib.parse, math, random, statistics This is a copy
88% identical to n8n-code-python — 388 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 756 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Code Node (Beta)
Expert guidance for writing Python code in n8n Code nodes.
⚠️ Important: JavaScript First
Recommendation: Use JavaScript for 95% of use cases. Only use Python when:
- You need specific Python standard library functions
- You're significantly more comfortable with Python syntax
- You're doing data transformations better suited to Python
Why JavaScript is preferred:
- Full n8n helper functions ($helpers.httpRequest, etc.)
- Luxon DateTime library for advanced date/time operations
- No external library limitations
- Better n8n documentation and community support
Quick Start
# Basic template for Python Code nodes
items = _input.all()
# Process data
processed = []
for item in items:
processed.append({
"json": {
**item["json"],
"processed": True,
"timestamp": datetime.now().isoformat()
}
})
return processed
Essential Rules
- Consider JavaScript first - Use Python only when necessary
- Access data:
_input.all(),_input.first(), or_input.item - CRITICAL: Must return
[{"json": {...}}]format - CRITICAL: Webhook data is under
_json["body"](not_jsondirectly) - CRITICAL LIMITATION: No external libraries (no requests, pandas, numpy)
- Standard library only: json, datetime, re, base64, hashlib, urllib.parse, math, random, statistics
Mode Selection Guide
Same as JavaScript - choose based on your use case:
Run Once for All Items (Recommended - Default)
Use this mode for: 95% of use cases
- How it works: Code executes once regardless of input count
- Data access:
_input.all()or_itemsarray (Native mode) - Best for: Aggregation, filtering, batch processing, transformations
- Performance: Faster for multiple items (single execution)
# Example: Calculate total from all items
all_items = _input.all()
total = sum(item["json"].get("amount", 0) for item in all_items)
return [{
"json": {
"total": total,
"count": len(all_items),
"average": total / len(all_items) if all_items else 0
}
}]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 756 lines · 52 tokens per session scan A 64df97c1be5d
n8n-code-python is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 17d ago), licensed MIT. It adds 52 tokens to every session and 4,557 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to n8n-code-python, differing in 388 lines, and is treated as a copy.
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