Agent skill

Marimo Pair

by cosanlab in cosanlab/nltools

Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact.

MITAuto-check passedData & Analytics

Install Marimo Pair

skills CLI
$ npx skills add cosanlab/nltools --skill marimo-pair -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cosanlab/nltools marimo-pair --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cosanlab/nltools.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/marimo-pair .claude/skills/marimo-pair && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
marimo-pair
GitHub stars
131
Used in
1 other repo
Token cost
~3k tokens
SKILL.md length
1,469 words
Files
7 (incl. scripts)
Skills in repo
3
Repo updated
First seen
Licence
MIT

At a glance

Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact.

  • The user wants to start a marimo notebook
  • SKILL.md covers Philosophy, Prerequisites, Troubleshooting and How to Discover Servers and…, plus 5 more sections
  • Runs Shell scripts from its folder; calls bash and uv; needs MARIMO_TOKEN
  • Work in an active marimo session

What it does

Marimo Pair is an agent skill from cosanlab/nltools. Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact. Use when the user wants to start a marimo notebook or work in an active marimo session.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts (for example `reference/finding-marimo.md`, `reference/gotchas.md` and `reference/notebook-improvements.md`).

It sits in Data & Analytics, covering Jupyter notebooks. It works with marimo and Python. The repository describes itself as: Python toolbox for analyzing imaging data. The licence is MIT.

When your agent uses it

  • The user wants to start a marimo notebook
  • Work in an active marimo session

Example prompts

  • “/marimo-pair”

Requirements

  • Python 3
  • A Bash shell
  • A credential in MARIMO_TOKEN
  • Pre-approved tools (allowed-tools): Bash(bash **/scripts/discover-servers.sh *), Bash(bash **/scripts/execute-code.sh *), Read

What it can do on your machine

Read from SKILL.md and the folder at commit cf366c2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(bash **/scripts/discover-servers.sh *)
    • Bash(bash **/scripts/execute-code.sh *)
    • Read

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • bash
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • MARIMO_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Marimo Pair loads about 3k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 1,469 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from cosanlab/nltools at commit cf366c2, republished under its MIT licence (© cosanlab). 1,469 words, ~2,953 tokens.

Download SKILL.mdSave it as .claude/skills/marimo-pair/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
marimo-pair
description
Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact. Use when the user wants to start a marimo notebook or work in an active marimo session.
allowed-tools
Bash(bash **/scripts/discover-servers.sh *), Bash(bash **/scripts/execute-code.sh *), Read

marimo Pair Programming Protocol

This skill gives you full access to a running marimo notebook. You can read cell code, create and edit cells, install packages, run cells, and inspect the reactive graph — all programmatically. The user sees results live in their browser while you work through bundled scripts or MCP.

Philosophy

marimo notebooks are a dataflow graph — cells are the fundamental unit of computation, connected by the variables they define and reference. When a cell runs, marimo automatically re-executes downstream cells. You have full access to the running notebook.

  • Cells are your main lever. Use them to break up work and choose how and when to bring the human into the loop. Not every cell needs rich output — sometimes the object itself is enough, sometimes a summary is better. Match the presentation to the intent.
  • Understand intent first. When clear, act. When ambiguous, clarify.
  • Follow existing signal. Check imports, pyproject.toml, existing cells, and dir(ctx) before reaching for external tools.
  • Stay focused. Build first, polish later — cell names, layout, and styling can wait.

Prerequisites

How to invoke marimo

Only servers started with --no-token register in the local server registry and are auto-discoverable — starting without a token makes discovery easier. If a server has a token, set the MARIMO_TOKEN environment variable before calling the execute script (avoids leaking the token in process listings). The right way to invoke marimo depends on context (project tooling, global install, sandbox mode). See finding-marimo.md for the full decision tree.

Do NOT use --headless unless the user asks for it. Omitting it lets marimo auto-open the browser, which is the expected pairing experience. If the user explicitly requests headless, offer to open http://localhost:<port> in their browser (open on macOS, xdg-open on Linux, start on Windows).

Troubleshooting

SyntaxError or ImportError from execute-code.sh

Code runs inside the running marimo kernel — execute-code.sh POSTs it over HTTP and never invokes a local Python. So errors here are not caused by the local Python version, missing venv, or uv vs pip — they're problems with the code being sent. Fix the code (use a heredoc for anything multiline; don't try to one-line compound statements with ;).

User keeps getting prompted to allow Bash commands

The skill declares allowed-tools in its frontmatter, but Claude Code may still prompt for each Bash call. To fix this, the user should add the absolute paths to the scripts to their .claude/settings.json (project-level) or ~/.claude/settings.json (global):

json
{
  "permissions": {
    "allow": [
      "Bash(bash /absolute/path/to/skills/marimo-pair/scripts/discover-servers.sh *)",
      "Bash(bash /absolute/path/to/skills/marimo-pair/scripts/execute-code.sh *)"
    ]
  }
}

How to Discover Servers and Execute Code

Two operations: discover servers and execute code.

OperationScriptMCP
Discover serversbash scripts/discover-servers.shlist_sessions() tool
Execute codebash scripts/execute-code.sh -c "code"execute_code(code=..., session_id=...) tool
Execute code (multiline)bash scripts/execute-code.sh <<'EOF'same
Execute code (by URL)bash scripts/execute-code.sh --url http://localhost:2718 -c "code"same (with url param)

Scripts auto-discover sessions from the local server registry. Use --port to target a specific server when multiple are running, --session to target a specific session when multiple notebooks are open on the same server, or --url to skip discovery and connect to a server by URL (e.g. --url http://localhost:2718). On Windows, prefer direct --url when registry discovery is empty — see the next section for why. Set the MARIMO_TOKEN env var to authenticate when the server has token auth enabled (--token flag also works but exposes the token in process listings). If the server was started with --mcp, you'll have MCP tools available as an alternative.

Discovery finds nothing but the user has a server running?

Only --no-token servers are in the registry. If discovery comes up empty, the server likely has token auth — ask the user for the token and set it as the MARIMO_TOKEN environment variable.

On Windows (Git Bash / MSYS2), discovery can also come up empty even for a running --no-token server. If the user confirms marimo is reachable locally, fall back to --url http://127.0.0.1:<port> (ask for the port).

No servers running?

Always discover before starting. Background task "completed" notifications do not mean the server died — check the output or run discover first.

If no servers are found, read the user's intent — if they want a notebook, start one. Always start marimo as a background task (using run_in_background on the Bash tool) so the server automatically gets cleaned up when the session ends and doesn't block the conversation. See finding-marimo.md.

If there's no .py file yet, pick a descriptive filename based on context (e.g., exploration.py, analysis.py, dashboard.py). Don't ask — just pick something reasonable.

Avoid shell escaping issues. -c works for simple one-liners, but for multiline code or code with quotes/backticks/${}, use a heredoc or a file:

bash
# heredoc (single-quoted delimiter prevents shell interpolation)
bash scripts/execute-code.sh <<'EOF'
import marimo._code_mode as cm

async with cm.get_context() as ctx:
    ctx.create_cell("x = 1")
EOF

# file
bash scripts/execute-code.sh /tmp/code.py

# target a specific port (skips auto-selection when multiple servers run)
bash scripts/execute-code.sh --port 2718 -c "1 + 1"
Show full SKILL.md (720 more words)Show less

Executing Code

Every execute-code call runs inside the notebook's kernel. All cell variables are in scope — print(df.head()) just works. Nothing you define persists between calls (variables, imports, side-effects all reset), but you can freely introspect the notebook: inspect variables, test code snippets, check types and shapes. Use this to explore, prototype, and validate before committing anything to the notebook — then create cells to persist state and make results visible to the user.

To mutate the notebook's dataflow graph — create, edit, and delete cells, install packages, and run cells — use marimo._code_mode:

python
import marimo._code_mode as cm

async with cm.get_context() as ctx:
    cid = ctx.create_cell("x = 1")
    ctx.packages.add("pandas")
    ctx.run_cell(cid)

You must use async with — without it, operations silently do nothing. All ctx.* methods are synchronous — they queue operations and the context manager flushes them on exit. Do not await them.

The kernel supports top-level await, so use async with directly. Do not wrap calls in async def main(): ... + asyncio.run(main()) — it's unnecessary and easy to get wrong (compound statements like async with can't follow def name(): on the same line, so cramming it into a -c one-liner produces a SyntaxError).

Cells are not auto-executed. create_cell and edit_cell are structural changes only — use run_cell to queue execution.

code_mode is a tested, safe API for notebook mutations — prefer it for all structural changes. You also have access to marimo internals from the kernel, but treat that as a last resort and only with high confidence after exploration.

Edit cells through code_mode, never the file system. Direct file writes are silently lost. It is tempting to reach for Edit/Write for a small tweak since edit_cell requires the full new cell body. Don't — without --watch (off by default) the kernel never sees those edits and overwrites them on its next save, so the user sees nothing. (Read on the .py is okay, but content may lag the live kernel; prefer ctx.cells[target].code.)

UI state lives outside the reactive graph. Anywidget traitlets can be read or set directly (e.g., slider.value = 5). For mo.ui.* elements, use ctx.set_ui_value(element, new_value) inside code_mode.

First Step: Explore the API

The code_mode API can change between marimo versions. Explore it at the start of each session — dig deeper into anything you're unsure about.

python
import marimo._code_mode as cm
help(cm)

Guard Rails

Skip these and the UI breaks:

  • Install packages via ctx.packages.add(), not uv add or pip. The code API handles kernel restarts and dependency resolution correctly. Only fall back to external CLIs if the API is unavailable or fails.
  • Custom widget = anywidget. For bespoke visual components, use anywidget with HTML/CSS/JS. Composed mo.ui is fine for simple forms and controls. See rich-representations.md.
  • NEVER Edit, Write, or NotebookEdit the notebook .py file while a session is running. Direct writes are silently destroyed and never reach the user. marimo only watches the file with --watch, which is off by default. Without it, the kernel doesn't pick up file edits — and on its next save, the kernel writes its own state and clobbers yours. The user sees no change, you think the work landed, and the bug is invisible. Always use ctx.edit_cell(target, code=...) with the full new cell body — even for a one-character change. (Read is allowed, but disk content may lag the live kernel; for the current truth prefer ctx.cells[target].code.)
  • No temp-file deps in cells. pathlib.Path("/tmp/...") in cell code is a bug.
  • Avoid empty cells. Prefer edit_cell into existing empty cells rather than creating new ones. Clean up any cells that end up empty after edits.
  • Don't worry about cell names. Most cells don't need explicit names — see notebook-improvements.md.

Widgets and Reactivity

Anywidget state (traitlets) lives outside marimo's reactive graph. To hook a widget trait into the graph, pick one strategy per widget — never mix them:

  • mo.state + .observe() — you pick specific traits to bridge. Default choice.
  • mo.ui.anywidget() — wraps all synced traits into one reactive .value. Convenient but coarser.

Read rich-representations.md before wiring either.

Keep in Mind

  • The user is editing too. The notebook can change between your calls — re-inspect notebook state if it's been a while since you last looked.
  • Deletions are destructive. Deleting a cell removes its variables from kernel memory — restoring means recreating the cell and re-running it and its dependents. If intent seems ambiguous, ask first.
  • Installing packages changes the project. ctx.packages.add() adds real dependencies — confirm when it's not obvious from context.

References

© cosanlab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts) in .claude/skills/marimo-pair of cosanlab/nltools.

  • SKILL.md
  • reference/finding-marimo.md
  • reference/gotchas.md
  • reference/notebook-improvements.md
  • reference/rich-representations.md
  • scripts/discover-servers.sh
  • scripts/execute-code.sh

Open the folder on GitHubat commit cf366c2

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in cosanlab/nltools, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Marimo Pair next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Marimo Pair compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Marimo Pair this skillcosanlab/nltools1311 repos~3kAutomated safety check: PassMIT
Wasm Compatibilityericmjl/llamabot1822 repos~1.6kAutomated safety check: PassNone
Marimo PairPhySpace/SimpleCADAPI140—~3kAutomated safety check: PassApache-2.0
Marimo Pair Vscodemarimo-team/marimo-lsp130—~2.7kAutomated safety check: PassApache-2.0
Marimobrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~2.8kAutomated safety check: PassCustom licence
Marimo Notebookminicoohei/ai-agent-camp347—~1.5kAutomated safety check: PassNone

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Works with

Questions about Marimo Pair

What does Marimo Pair do?

Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact. Marimo Pair is an agent skill from cosanlab/nltools. Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact.

When should I use Marimo Pair?

Marimo Pair fits situations like: the user wants to start a marimo notebook; work in an active marimo session.

How do I install Marimo Pair in Claude Code?

Run `npx skills add cosanlab/nltools --skill marimo-pair -a claude-code`. Or copy the skill folder (.claude/skills/marimo-pair in cosanlab/nltools) into .claude/skills/marimo-pair in your project. Claude Code loads it when a task matches its description.

How do I install Marimo Pair in Codex?

Run `npx skills add cosanlab/nltools --skill marimo-pair -a codex`. Or copy the skill folder (.claude/skills/marimo-pair in cosanlab/nltools) into .agents/skills/marimo-pair in your project. Codex loads it when a task matches its description.

Can I use Marimo Pair in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cosanlab/nltools --skill marimo-pair -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/marimo-pair, .gemini/skills/marimo-pair, .github/skills/marimo-pair and .opencode/skills/marimo-pair in your project.

What does Marimo Pair need to run?

Going by SKILL.md and its folder, Marimo Pair needs a shell for the scripts in its folder, the command-line tools its instructions call (bash and uv) and credentials named MARIMO_TOKEN. Our summary lists: Python 3; A Bash shell; A credential in MARIMO_TOKEN. Its frontmatter pre-approves these tools: Bash(bash **/scripts/discover-servers.sh *), Bash(bash **/scripts/execute-code.sh *), Read.

Does Marimo Pair access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Marimo Pair safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Marimo Pair use?

Marimo Pair is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Marimo Pair use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Marimo Pair?

Skills that share tags, products or a category with Marimo Pair: Wasm Compatibility (ericmjl/llamabot, 182 stars), Marimo Pair (PhySpace/SimpleCADAPI, 140 stars), Marimo Pair Vscode (marimo-team/marimo-lsp, 130 stars) and Marimo (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Marimo Pair?

cosanlab (a GitHub organization) maintains it in cosanlab/nltools, which has 131 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 1, 2026.

Source: cosanlab/nltools on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.