Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill jupyter-notebookgit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/jupyter-notebook)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/jupyter-notebook"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/jupyter-notebook/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/nousresearch/hermes-agent/jupyter-notebook"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/jupyter-notebook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 73 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 | $0.00018 | $0.01531 |
| Opus 5 | $0.00009 | $0.00766 |
| Sonnet 5 | $0.00004 | $0.00306 |
| Haiku 4.5 | $0.00002 | $0.00153 |
Grade A, and why
jupyter-notebook 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 11d 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 \ Copies of this mod
8 near-identical copies found in the catalogue:
- jupyter-notebook — 100% identical, 0 lines differ
- jupyter-notebook — 100% identical, 2 lines differ
- jupyter-notebook — 100% identical, 0 lines differ
- jupyter-live-kernel — 86% identical, 17 lines differ
- jupyter-live-kernel — 86% identical, 17 lines differ
- jupyter-live-kernel — 86% identical, 23 lines differ
- jupyter-live-kernel — 86% identical, 17 lines differ
- jupyter-live-kernel — 86% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jupyter Notebook (hamelnb live kernel)
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 hermes 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":"python"}}'
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.
- 11d ago First seen · 179 lines · 18 tokens per session scan A fea9fb88a35e
jupyter-notebook is a skill published in the GitHub repository NousResearch/hermes-agent (244,059 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,531 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
mem0
Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK…
rubyllm
Build and maintain Ruby or Rails applications with the RubyLLM AI framework. Use for chats, agents, tools, structured output, media generation, transcription, OCR, moderation, embeddings, reranking, Rails integration, and RubyLLM upgrades; not for contributing to the framework itself.
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
python-repl
Interactive Python REPL automation with common helpers and best practices.
python-math
Small Python utilities for math and text files.
alterlab-anndata
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the…