Work in user's live marimo notebook

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name: marimo-pair description: >- Work inside the user's live marimo notebook from the code editor: run Python in the same kernel the user does, inspect live notebook state, and commit durable notebook changes through code mode. Use whenever you create, analyze, or improve the user's marimo notebook.

marimo is a reactive Python runtime for building reproducible Python programs (marimo notebooks). Cells are connected by the variables they define and reference. Running a cell re-executes dependents in dataflow order. The active runtime holds the kernel namespace, cell state, and dataflow graph. The notebook (.py file) is the artifact the kernel writes from that state while a session is running.

A user interacts with the same runtime via a notebook UI with cells, outputs, and widgets.

WARNING. The active runtime is the source of truth. You are working inside the user's live notebook session. The kernel — not the .py file on disk — holds the truth about cells, variables, and the dataflow graph. Make every notebook change through marimo._code_mode (cm); direct file edits WILL NOT reach the live kernel or user, and the kernel may overwrite them on save. Reading disk is fine, but prefer ctx.cells[...].code for current cell code.

WARNING. Every notebook edit goes through code mode — no exceptions. When the user asks you to change the notebook (add, edit, delete, or run a cell; rename; change a value or a package), you MUST make that change through marimo._code_mode (cm) by running it with the execute_code tool. There is no separate file-editing path — execute_code + cm is the only way to reach the user's running session. Running ad-hoc Python with execute_code to explore or test is fine; persisting any change to the notebook is only ever done via cm.

Running Code in the Kernel

You are already attached to the user's running notebook — there is nothing to connect to or start. Use the execute_code tool to run Python in that kernel, passing your Python as the code argument. Everything you do — inspecting state, testing transformations, and persisting changes via cm — runs through execute_code in the scratchpad (see below).

Required First Kernel Command

Start every code-mode session with this dedicated execute_code call:

import marimo._code_mode as cm
help(cm)

Follow this order for every live kernel, including read-only tasks:

1. Run only the inspection command above.
2. Wait for successful help(cm) output.
3. Use cm.get_context() or another cm API in a later execute_code call.

Do not combine the inspection with task-specific code, and do not use another cm API before the inspection succeeds. This verifies the private, unstable API exposed by the marimo version in the user's active kernel.

Scratchpad Scope

execute_code evaluates Python in marimo's scratchpad: a temporary namespace with a shallow copy of the kernel globals. Notebook variables are available by name, but new top-level bindings and rebindings are discarded after each call. In-place mutations to notebook-owned objects can persist because those names still reference live objects.

Each call reports stdout and stderr from the scratchpad, plus console output from notebook cells it causes to run, including reactive descendants.

Ordinary Python

Use ordinary Python in the scratchpad to inspect variables, sample data, test transformations, probe APIs, check imports, and read widget state.

print(df.head())

x = 10
print(x)

Here df comes from notebook globals, while x is a scratchpad-local binding. x exists for this call only and WILL NOT be added to notebook globals.

Persist with cm

Top-level scratchpad assignments and rebindings are temporary. To persist work, including new variables, you MUST submit changes through marimo._code_mode (cm).

marimo._code_mode is a PRIVATE, UNSTABLE agent API (note the leading underscore). It exists for tools like this skill to drive a live kernel from the scratchpad. DO NOT import it from notebook cells, library code, or anything a user would run — methods can change or disappear across marimo versions and kernels. Treat every import marimo._code_mode as cm as scratchpad-only.

Open a code-mode context to queue notebook changes.

import marimo._code_mode as cm

async with cm.get_context() as ctx:
    cid = ctx.create_cell("x = df.head()")
    ctx.run_cell(cid)

The scratchpad supports top-level async code. Use async with directly; wrapping it in asyncio.run(...) is unnecessary and can conflict with the kernel's event loop.

After this block exits and the new cell runs, x is notebook state. Later scratchpad calls can read x by name. Code later in the same scratchpad call should read ctx.globals["x"], because the scratchpad namespace was copied before the cell ran.

Inside the context, queued mutation methods are synchronous. Call them directly; do not await them. Each call queues an operation for marimo to apply when the context exits normally. If the block raises, the queue is discarded.

On clean exit, marimo applies packages, validates and applies structural cell changes, runs queued cells, then may run dependents. Validation is only structural since queued cell runs can still error. create_cell and edit_cell change notebook structure only. Use run_cell to execute.

create_cell currently defaults to hide_code=True, which collapses the code editor in the UI. Pass hide_code=False if the user wants created cells to be visible without manually expanding them.

Marimo Rules

marimo imposes a small contract on notebook code so it can keep the notebook as a directed acyclic graph (DAG):

• No cycles - cells cannot depend on each other in a cycle.
• No public redefinitions across cells - each name has one owning cell.
• No wildcard imports - import * prevents static analysis of definitions.

These rules keep the kernel, UI, and saved artifact consistent.

When cm submits a cell body, marimo parses its top-level definitions and references. A top-level name enters the graph unless it is private with a leading underscore.

# Public definitions: values, total, i, value, mean
values = np.array([1, 2, 3])
total = 0
for i, value in enumerate(values):
    total += value
mean = total / len(values)
mean

# Public definition: mean
_values = np.array([1, 2, 3])
_total = 0
for _i, _value in enumerate(_values):
    _total += _value
mean = _total / len(_values)
mean

Use private names for intermediates that no other cell should read. Public names define the notebook-level dataflow. If a cm edit violates the contract, marimo rejects the structural change and returns the validation error.

The Notebook's Shape

A notebook is an ordered collection of cells. ctx.cells is the document view and ctx.graph is the dataflow view.

for cell in ctx.cells:
    cell  # .id, .code, .name, .config, .status, .errors

ctx.cells["setup"]         # by name
ctx.cells[0]               # by position
list(ctx.cells.keys())     # all IDs, in notebook order

Cell IDs are opaque strings which can be queried from the notebook or captured from cm return values:

cid = ctx.create_cell("df = pd.read_csv('data.csv')")
print(cid)   # e.g. 'Hbol'

Alternatively, cells can be assigned and referenced by name. The graph can be used to understand its role in the dataflow.

for cid, impl in ctx.graph.cells.items():
    impl  # .defs, .refs   (sets of public names)

ctx.graph.descendants(cid)   # cells that re-run when this one changes
ctx.graph.ancestors(cid)     # cells this one depends on

In marimo, deletes are destructive so it can be useful to query the descendants prior to deleting to understand it's impact.

Writing Notebook Changes

The graph contract keeps marimo able to run and save the notebook. Passing those checks alone does not guarantee a useful artifact. Committed cells should still be readable, rerunnable, and editable.

Make durable edits that reuse the notebook's existing names, imports, dependencies, and UI model. Don't be lazy. Avoid one-off workarounds that pass cm validation but leave a brittle notebook.

Cell Bodies

Submit the code that belongs in the cell.

• Submit cell contents - create_cell and edit_cell take cell contents, not saved-file @app.cell wrappers.
• Read before replacing - for now, another editor may change a cell between scratchpad calls. Before edit_cell, read the current body from ctx.cells[...] and submit the full replacement.
• Reuse notebook imports - if np already exists, use it or edit the owning import cell. DO NOT add import numpy as _np just to bypass the graph.
• Define public names intentionally - use public names for values later cells should reference. Use private _name bindings or function locals for same-cell intermediates.
• Define each public name once - a public name has one owning cell. Reassigning it in another cell fails with Multiply-defined names; edit the owning cell or give the result a new name. Load the gotchas capability for more traps.
• Run cells deliberately - create_cell and edit_cell change structure only. Queue ctx.run_cell(...) when the cell should execute.

Prefer cm-Managed Changes

Use cm APIs when they exist. Avoid direct file edits, shell package commands, and scratchpad-only state for changes that should persist.

• Persist only through cm - never try to change the notebook by writing the .py file; execute_code + cm is the only path that reaches the live session. Use ctx.edit_cell(...) even for small changes.
• Manage packages through cm - use ctx.packages.add() or ctx.packages.remove() instead of direct uv or pip; confirm non-obvious dependency changes.
• Avoid transient paths - persisted cells should not depend on /tmp/... unless the work is intentionally transient.
• Delete deliberately - deleting a cell removes globals it defines. Reuse empty cells when convenient and delete cells left empty after edits.

UI and Widgets

Inspect the object before changing it. Different UI objects update through different paths.

• Set mo.ui.* through cm - use ctx.set_ui_value(element, value) inside cm.get_context().
• Set anywidget traitlets directly - synced traitlets are Python attributes, for example widget.value = 5.

For designing custom visual or interactive output, load the rich-representations capability.

On-demand references

Load these with the load_capability tool when you need deeper guidance. Do not read reference files from disk.

• gotchas — name redefinition, cached module proxies, and notebook traps
• rich-representations — custom widgets and visualizations
• notebook-improvements — improving existing notebooks