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 yuchenbigpen1/normies --skill n8n-code-pythongit clone --depth 1 https://github.com/yuchenbigpen1/normiesWrote 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/yuchenbigpen1/normies/n8n-code-python)<a href="https://agentmods.dev/skills/yuchenbigpen1/normies/n8n-code-python"><img src="https://agentmods.dev/badge/skills/yuchenbigpen1/normies/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/yuchenbigpen1/normies/n8n-code-python"><img src="https://agentmods.dev/badge/skills/yuchenbigpen1/normies/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 | $0.04565 |
| Opus 5.5 | $0.00021 | $0.01826 |
| Sonnet 5.5 | $0.00010 | $0.00913 |
| Haiku 4.5 | $0.00005 | $0.00456 |
Grade A, and why
n8n-code-python scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
6. **Standard library only**: json, datetime, re, base64, hashlib, urllib.parse, math, random, statistics This is a copy
86% identical to n8n-code-python — 367 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 749 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Code Node (Beta)
Expert guidance for writing Python code in n8n Code nodes.
⚠️ Important: JavaScript First
Recommendation: Use JavaScript for 95% of use cases. Only use Python when:
- You need specific Python standard library functions
- You're significantly more comfortable with Python syntax
- You're doing data transformations better suited to Python
Why JavaScript is preferred:
- Full n8n helper functions ($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 ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 749 lines · 52 tokens per session scan A fa65b75a3605
n8n-code-python is a skill published in the GitHub repository yuchenbigpen1/normies (4 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 52 tokens to every session and 4,565 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to n8n-code-python, differing in 367 lines, and is treated as a copy.
Other skills, from other repositories
py-reviewer
Review selected Python code for typing, async and exception behavior, resource ownership, data access, framework boundaries, and concrete contract defects in the project's actual runtime.
coding
A coding guide for writing and running Python programs in a sandbox. It requires scripts to be small and reproducible, with their actual output or errors reported.
neo4j-driver-python-skill
Neo4j Python Driver v6 — driver lifecycle, executequery, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver…
generating-python-installer
Commercial-grade Python installer expert for Windows: Nuitka extreme compilation, dist slimming, DLL footprint analysis, and Inno Setup packaging to ship the smallest, fastest installers. Use when a Python app must ship as a minimal, fast-starting Windows installer; not for basic script-to-exe conversion. Relevant…
fastapi-async-microservices
Design and develop production-grade asynchronous microservices using FastAPI, Python 3.12+, Pydantic v2 Settings and Schemas, SQLAlchemy 2.0 async sessions, dependency injection, lifespan state management, structured JSON logging, and connection pooling. Trigger when building async Python APIs, microservices, or…
dsp-audio-filter-effects
Apply digital signal processing (DSP) algorithms in Python and C++ for audio filtering (IIR, FIR, Butterworth, Chebyshev), spectral analysis via Fast Fourier Transform (FFT), dynamic range compression, and modulation effects. Trigger when developing audio DSP algorithms, sound effects, or analyzing audio signals.