MiMoCode is a terminal-based AI coding assistant that reads and writes code, runs commands, manages Git, and remembers project context across sessions. Developers use it to work with software projects through a command-line interface and connect it to language-model providers; the catalogue includes skills and instructions for it.
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 XiaomiMiMo/MiMo-Code --skill jupyter-notebooksgit clone --depth 1 https://github.com/XiaomiMiMo/MiMo-CodeWrote 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/xiaomimimo/mimo-code/jupyter-notebooks)<a href="https://agentmods.dev/skills/xiaomimimo/mimo-code/jupyter-notebooks"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/jupyter-notebooks/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/xiaomimimo/mimo-code/jupyter-notebooks"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/jupyter-notebooks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00049 | $0.01541 |
| Opus 5.5 | $0.00020 | $0.00616 |
| Sonnet 5.5 | $0.00010 | $0.00308 |
| Haiku 4.5 | $0.00005 | $0.00154 |
Grade A, and why
jupyter-notebooks scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Related Skills
Use $validate-data when notebook results support a recommendation, shared claim, or decision.
Jupyter Notebooks
Create clean, reproducible Jupyter notebooks that are easy to skim, rerun, and handoff. Treat the notebook as a reader-facing analysis artifact, not a scratchpad dump. Notebook work is not complete until the notebook executes successfully top-to-bottom, or the execution gap is called out with the exact validation steps needed to reproduce it.
Workflow
-
Lock the notebook mode and scope.
Decide whether the notebook is an analysis report, experiment log, diagnostic notebook, data-quality check, market-sizing calculation, model exploration, tutorial, or companion artifact for a report. Identify the reader, decision, expected handoff, required inputs, and whether the task calls for a new notebook or targeted edits to an existing one.
-
Inspect or scaffold with notebook-safe tooling.
Prefer JupyterLab,
nbformat,nbclient, or an existing scaffold utility over hand-editing raw JSON. When editing an existing notebook, preserve its intent and minimize JSON churn. Avoid reordering cells unless it clearly improves the top-to-bottom story. If raw JSON editing is unavoidable, validate the notebook structure before finishing. -
Structure the notebook for the chosen mode.
For analytical notebooks, default to:
## tl;dr## Context & Methods## Data## Results## Takeaways
Write
tl;drand takeaways after reviewing executed outputs. Use concrete observed values, visible patterns, rows, or charts, not assumptions. Include a### Key Assumptionssubsection inContext & Methodswhen assumptions affect correctness.For tutorials or walkthroughs, adapt the same discipline to a teaching flow:
## Goal## Setup## Steps## Checks## Next Steps
-
Build a clear data and computation path.
Separate setup, imports, parameters, data loading, data preparation, calculations, visualizations, and interpretation. If the notebook uses both SQL and Python, keep complex SQL in SQL cells or separate query files rather than large embedded Python strings unless there is a clear reason. Use descriptive variable names and keep each code cell focused on one step.
What ships with it
1 file 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 · 129 lines · 49 tokens per session scan A 8303d33041a5
jupyter-notebooks is a skill published in the GitHub repository XiaomiMiMo/MiMo-Code (13,696 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 1,541 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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docs-scraper
A tool-assisted method for downloading an entire technical documentation website as local Markdown files. It first checks whether the site offers ready-made Markdown or text bundles before crawling pages.
docutranslate
Use when translating documents locally via LLM — PDF, Word, Excel, Markdown, SRT subtitles with format preservation. DocuTranslate: LLM-powered multi-format local file translation tool with MCP server support.
parse-cv
Pre-process a CV/profile file (PDF, DOCX, ODT, RTF) into plain text BEFORE feeding it to the LLM context. Reduces token cost by 5-10x on long CVs and yields more reliable extraction than reading binary PDFs directly via multimodal vision. The Assistente calls this skill on every uploaded document in…