Moltis is a persistent personal agent server written in Rust that runs on hardware controlled by its user. It provides an AI agent with sandboxed command execution, model-provider connections, memory, voice, scheduling, messaging integrations, browser automation, and MCP tools. Its catalogue add-ons extend the agent’s workflows and available tools.
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 moltis-org/moltis --skill jupyter-live-kernelgit clone --depth 1 https://github.com/moltis-org/moltisWrote 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/moltis-org/moltis/jupyter-live-kernel)<a href="https://agentmods.dev/skills/moltis-org/moltis/jupyter-live-kernel"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/jupyter-live-kernel/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/moltis-org/moltis/jupyter-live-kernel"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/jupyter-live-kernel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 74 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.01412 |
| Opus 5 | $0.00038 | $0.00706 |
| Sonnet 5 | $0.00015 | $0.00282 |
| Haiku 4.5 | $0.00008 | $0.00141 |
Grade A, and why
jupyter-live-kernel scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST http://127.0.0.1:8888/api/sessions \ This is a copy
77% identical to jupyter-notebook — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jupyter Live Kernel (hamelnb)
Gives you a stateful Python REPL via a live Jupyter kernel. Variables persist
across executions. Use this instead of execute_code when you need to build up
state incrementally, explore APIs, inspect DataFrames, or iterate on complex code.
When to Use This vs Other Tools
| Tool | Use When |
|---|---|
| This skill | Iterative exploration, state across steps, data science, ML, "let me try this and check" |
execute_code |
One-shot scripts needing agent tool access (web_search, file ops). Stateless. |
terminal |
Shell commands, builds, installs, git, process management |
Rule of thumb: If you'd want a Jupyter notebook for the task, use this skill.
Prerequisites
- uv must be installed (check:
which uv) - JupyterLab must be installed:
uv tool install jupyterlab - A Jupyter server must be running (see Setup below)
Setup
The hamelnb script location:
SCRIPT="$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.
- 10d ago First seen · 169 lines · 76 tokens per session scan A 55d8e23937c6
jupyter-live-kernel is a skill published in the GitHub repository moltis-org/moltis (2,847 stars, last pushed 6d ago), licensed MIT. It adds 76 tokens to every session and 1,412 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 77% identical to jupyter-notebook, differing in 38 lines, and is treated as a copy.
Other skills, from other repositories
audit-training-experiment
Audits an ML training experiment for reproducibility, evidence integrity, leakage, checkpoint identity, evaluation validity, and overclaimed conclusions. Use before accepting results, spending on a longer run, publishing artifacts, or handing work to another agent.
close-out-ml-experiment
Preserves positive or negative ML experiment evidence, reconciles report discrepancies, records the canonical decision and limitations, and prepares a concise handoff. Use when stopping an experiment, rejecting a prompt/model, or freezing a result before the next iteration.
cmd_gan_build
GAN pipeline: run gan-planner → gan-generator → gan-evaluator in sequence. Loops until evaluator score ≥ 7.0.
saelens
Train sparse autoencoders to interpret model features.
add-ollama-tool
Add Ollama MCP server so the container agent can call local models and optionally manage the Ollama model library.
batch-processing-clinical-text
Run large-scale batch NER, PII extraction, or de-identification over many clinical notes on-device with OpenMed, with sharding, checkpointing, resumability, and append-only JSONL output. Use when the user needs to process a corpus or folder of notes, de-identify a dataset, run NER over thousands of documents, build a…