OpenGauss is a project-scoped Lean workflow orchestrator that gives coding agents a command-line interface for managing formal proof and formalization tasks. It is used with Lean projects to coordinate agents, tooling, backend sessions, and workflows supplied by lean4-skills. The catalogue add-ons operate these Gauss-native workflows.
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 math-inc/OpenGauss --skill jupyter-live-kernelgit clone --depth 1 https://github.com/math-inc/OpenGaussWrote 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/math-inc/opengauss/jupyter-live-kernel)<a href="https://agentmods.dev/skills/math-inc/opengauss/jupyter-live-kernel"><img src="https://agentmods.dev/badge/skills/math-inc/opengauss/jupyter-live-kernel.svg" alt="Measured on agentmods" 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 | $0.00076 | $0.01431 |
| Opus 5 | $0.00038 | $0.00715 |
| Sonnet 5 | $0.00015 | $0.00286 |
| Haiku 4.5 | $0.00008 | $0.00143 |
Grade A, and why
jupyter-live-kernel scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST http://127.0.0.1:8888/api/sessions \ This is a copy
78% identical to jupyter-notebook — 31 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jupyter Live Kernel (hamelnb)
Gives you a stateful Python REPL via a live Jupyter kernel. Variables persist
across executions. Use this instead of execute_code when you need to build up
state incrementally, explore APIs, inspect DataFrames, or iterate on complex code.
When to Use This vs Other Tools
| Tool | Use When |
|---|---|
| This skill | Iterative exploration, state across steps, data science, ML, "let me try this and check" |
execute_code |
One-shot scripts needing gauss tool access (web_search, file ops). Stateless. |
terminal |
Shell commands, builds, installs, git, process management |
Rule of thumb: If you'd want a Jupyter notebook for the task, use this skill.
Prerequisites
- uv must be installed (check:
which uv) - JupyterLab must be installed:
uv tool install jupyterlab - A Jupyter server must be running (see Setup below)
Setup
The hamelnb script location:
SCRIPT="$HOME/.agent-skills/hamelnb/skills/jupyter-live-kernel/scripts/jupyter_live_kernel.py"
If not cloned yet:
git clone https://github.com/hamelsmu/hamelnb.git ~/.agent-skills/hamelnb
Starting JupyterLab
Check if a server is already running:
uv run "$SCRIPT" servers
If no servers found, start one:
jupyter-lab --no-browser --port=8888 --notebook-dir=$HOME/notebooks \
--IdentityProvider.token='' --ServerApp.password='' > /tmp/jupyter.log 2>&1 &
sleep 3
Note: Token/password disabled for local agent access. The server runs headless.
Creating a Notebook for REPL Use
If you just need a REPL (no existing notebook), create a minimal notebook file:
mkdir -p ~/notebooks
Write a minimal .ipynb JSON file with one empty code cell, then start a kernel session via the Jupyter REST API:
curl -s -X POST http://127.0.0.1:8888/api/sessions \
-H "Content-Type: application/json" \
-d '{"path":"scratch.ipynb","type":"notebook","name":"scratch.ipynb","kernel":{"name":"python3"}}'
Core Workflow
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.
- 8d ago First seen · 172 lines · 76 tokens per session scan A 0b6c8aeb49d0
jupyter-live-kernel is a skill published in the GitHub repository math-inc/OpenGauss (1,260 stars, last pushed 5mo ago), licensed MIT. It adds 76 tokens to every session and 1,431 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 78% identical to jupyter-notebook, differing in 31 lines, and is treated as a copy.
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