marimo

name	marimo
description	Use when working with marimo notebooks — creating, editing, debugging, converting from Jupyter, or pairing with a running marimo server.
user-invocable	false

Contents

• Editing and Verification Enforcement
• Key Concepts
• Cell Structure
• Editing Rules
• Core CLI Commands
• Export Commands
• Live Session (marimo-pair)
• Data and Visualization
• Debugging Workflow
• Common Issues
• Additional Resources

Marimo Reactive Notebooks

Marimo is a reactive Python notebook where cells form a DAG and auto-execute on dependency changes. Notebooks are stored as pure .py files.

Editing and Verification Enforcement

IRON LAW #1: NEVER MODIFY CELL DECORATORS OR SIGNATURES

Only edit code INSIDE @app.cell function bodies. This is not negotiable.

NEVER modify:

• Cell decorators (@app.cell)
• Function signatures (def _(deps):)
• Return statements structure (trailing commas required)

ALWAYS verify:

• All used variables are in function parameters
• All created variables are in return statement
• Trailing comma for single returns: return var,

IRON LAW #2: NO EXECUTION CLAIM WITHOUT OUTPUT VERIFICATION

Before claiming ANY marimo notebook works:

1. VALIDATE syntax and structure: marimo check notebook.py
2. EXECUTE with outputs: marimo export ipynb notebook.py -o __marimo__/notebook.ipynb --include-outputs
3. VERIFY using notebook-debug skill's verification checklist
4. CLAIM success only after verification passes

This is not negotiable. Skipping execution and output inspection is NOT HELPFUL — the user gets a notebook that fails when they open it.

Marimo Facts

• marimo check validates syntax and structure only — it never executes cells. Claiming a notebook works because check passed is an unverified claim presented as fact.
• Reactivity propagates every edit through the DAG: a one-line change re-executes all dependent cells. Verifying only the edited cell misses downstream breakage — counterproductive on its own terms.
• Wrong dependencies or missing returns break reactivity silently: no error at edit time, only a NameError when a dependent cell runs. Validate that all used variables are in params AND all created variables are in returns.
• A variable created but not returned raises NameError in every cell that depends on it.
• Python treats return var as returning the bare value, which breaks unpacking — single returns require the trailing comma (return var,).
• Marimo re-runs a cell when a variable in its dependency DAG changes. Regenerating a data file changes no variable — t("table7") is the same call on the same code — so the runtime correctly re-runs nothing, and --watch watches the notebook .py, not the data directory. A data-only change is therefore completely invisible to a live session until you re-run the cells yourself. Worse, every check you can run still passes: the parquet on disk is correct, t("table7") called from the kernel re-reads disk and returns the NEW data, all cells report status=idle with no errors, and a fresh HTML export is correct. Only the rendered output already sitting in the user's browser is stale — so "I queried the kernel and it's correct" is not evidence the user can see it, and you can verify a change thoroughly and report it truthfully while the person watching the screen sees the old numbers.

Red Flags — STOP If About To:

• Edit a @app.cell decorator or def _(...) signature → STOP. Marimo manages these; edit only the function body.
• Claim done after only marimo check → STOP. Execution with --include-outputs is required.
• Claim the notebook works from reading the code → STOP. Reactive correctness shows only at runtime.
• Define a variable that another cell already defines → STOP. One variable = one cell.
• Report a regenerated data file as something the user can see, without having re-run the cells → STOP. Nothing re-ran; their browser still shows the old numbers.

Editing Checklist

Before every marimo edit:

Structure Validation:

• Only edit code INSIDE @app.cell function bodies
• Do NOT modify decorators or signatures
• Verify all used variables are in function parameters
• Verify all created variables are in return statement
• Ensure trailing comma used for single returns
• Ensure no variable redefinitions across cells

Syntax Validation:

• Execute marimo check notebook.py
• Verify no syntax errors reported
• Verify no undefined variable warnings
• Verify no redefinition warnings

Runtime Verification:

• Execute with marimo export ipynb notebook.py -o __marimo__/notebook.ipynb --include-outputs
• Verify export succeeded (exit code 0)
• Verify output ipynb exists and is non-empty
• Apply notebook-debug verification checklist
• Verify no tracebacks in any cell
• Verify all cells executed (execution_count not null)
• Verify outputs match expectations

Only after ALL checks pass:

• Claim "notebook works"

Gate Function: Marimo Verification

Follow this sequence for EVERY marimo task:

1. EDIT     → Modify code inside @app.cell function bodies only
2. CHECK    → marimo check notebook.py
3. EXECUTE  → marimo export ipynb notebook.py -o __marimo__/notebook.ipynb --include-outputs
4. INSPECT  → Use notebook-debug verification
5. VERIFY   → Outputs match expectations
6. CLAIM    → "Notebook works" only after all gates passed

NEVER skip verification gates. Marimo's reactivity means changes propagate unpredictably.

Key Concepts

• Reactive execution: Cells auto-update when dependencies change
• No hidden state: Each variable defined in exactly one cell
• Pure Python: .py files, version control friendly
• Cell structure: @app.cell decorator pattern

Cell Structure

import marimo

app = marimo.App()

@app.cell
def _(pl):  # Dependencies as parameters
    df = pl.read_csv("data.csv")
    return df,  # Trailing comma required for single return

@app.cell
def _(df, pl):
    summary = df.describe()
    filtered = df.filter(pl.col("value") > 0)
    return summary, filtered  # Multiple returns

Editing Rules

• Edit code INSIDE @app.cell functions only
• Never modify cell decorators or function signatures
• Variables cannot be redefined across cells
• All used variables must be returned from their defining cell
• Markdown cells: Always wrap $ in backticks - mo.md("Cost: $50") not mo.md("Cost: $50")

Core CLI Commands

Command	Purpose
marimo edit notebook.py	marimo: Open notebook in browser editor for interactive development
marimo run notebook.py	marimo: Run notebook as executable app
marimo check notebook.py	marimo: Validate notebook structure and syntax without execution
marimo convert notebook.ipynb	marimo: Convert Jupyter notebook to marimo format

Export Commands

# marimo: Export to ipynb with code only
marimo export ipynb notebook.py -o __marimo__/notebook.ipynb

# marimo: Export to ipynb with outputs (runs notebook first)
marimo export ipynb notebook.py -o __marimo__/notebook.ipynb --include-outputs

# marimo: Export to HTML (runs notebook by default)
marimo export html notebook.py -o __marimo__/notebook.html

# marimo: Export to HTML with auto-refresh on changes (live preview)
marimo export html notebook.py -o __marimo__/notebook.html --watch

Key difference: HTML export runs the notebook by default. ipynb export does NOT - use --include-outputs to run and capture outputs.

Tip: Use __marimo__/ folder for all exports (ipynb, html). The editor can auto-save there.

Live Session (marimo-pair)

For working inside a running marimo notebook kernel — executing code, creating/editing cells, and building notebooks interactively — use the marimo-pair protocol. Full details: marimo-pair/SKILL.md.

Starting a Server

See marimo-pair/reference/finding-marimo.md for the full decision tree. Quick start:

# pixi project (our standard)
pixi run marimo edit notebook.py --no-token --watch

# uv project
uv run marimo edit notebook.py --no-token --watch

# standalone / sandbox
uvx marimo@latest edit notebook.py --no-token --watch --sandbox

Always use --watch so the server detects file edits and reloads automatically. Without it, file changes are invisible to the browser and the user sees stale content.

Always start as a background task (run_in_background) so the server doesn't block the conversation. Do NOT use --headless unless asked — let marimo open the browser.

Remote Box? Bind to the Tailnet, Don't Ask for SSH Forwarding

marimo edit binds 127.0.0.1 by default. When the notebook runs on a remote host and the user is on SSH, that is unreachable — they get nothing, and the obvious next move (tell them to set up LocalForward) costs them a config edit and a reconnect before they can look at anything.

Bind to the machine's Tailscale address instead. It works immediately, from any tailnet device including a phone, with no client-side change:

TS_IP=$(tailscale ip -4 2>/dev/null | head -1)
setsid nohup marimo edit notebook.py --no-token --watch --headless \
  --host "$TS_IP" --port 2718 > /tmp/marimo.log 2>&1 < /dev/null & disown

# confirm it is actually reachable — a bind is not a connection
ss -ltn | grep 2718
curl -s -o /dev/null -w '%{http_code}\n' "http://$TS_IP:2718"    # want 200

Then hand the user http://$TS_IP:2718. --headless is correct here (the opposite of the local-box default above): a remote host has no browser to open.

Tear it down when the review closes. A --no-token server left running is an open notebook kernel on the tailnet, and the next session's discover-servers.sh finds a stale one bound to a notebook nobody is reviewing.

# record the pid at launch — this is the safe handle
echo $! > /tmp/marimo.pid
kill "$(cat /tmp/marimo.pid)"

IRON LAW #3: import marimo GOES IN THE FIRST ~400 BYTES

Never put a module docstring, licence header, or comment block in front of import marimo. This is not negotiable.

marimo decides whether a .py file is a notebook by scanning only the head of the file. Push the signature past that window and the file is still a perfectly valid notebook that runs, checks, and exports — it simply stops appearing in the workspace listing, and there is no error anywhere to explain why.

Measured on 0.23.4 by bisection, identical files differing only in a leading docstring:

import marimo at byte	workspace listing
0, 167, 246, 325, 404	listed
483, 562, 641	hidden

Put explanatory prose in an mo.md intro cell instead — a module docstring is invisible in the rendered notebook anyway, so the "documentation" it buys costs the file its discoverability and shows the reader nothing.

If a notebook you just wrote is missing from the workspace, check the byte offset of import marimo before anything else:

python3 -c "print(open('nb.py').read().index('import marimo'))"   # want < 400

Diagnose the listing directly rather than guessing at the server — the API answers precisely, including the root it is scanning:

TOK=$(curl -s "http://$HOST:$PORT" | grep -oP '(?<=data-token=")[^"]+' | head -1)
curl -s -X POST -H 'Content-Type: application/json' -H "Marimo-Server-Token: $TOK" \
  -d '{}' "http://$HOST:$PORT/api/home/workspace_files"

Server Lifecycle Facts

• marimo's workspace file browser roots at the process cwd, not at the path argument. Running marimo edit notebooks/ from the repo root serves that directory but browses the root, so the workspace lists nothing while "recent notebooks" still shows whatever was opened before — which reads as a marimo bug rather than a launch mistake. cd into the directory first.
• A notebook whose import marimo sits past ~400 bytes is invisible in the workspace listing while remaining fully valid — it runs, marimo check passes, export works. Iron Law #3 above.
• marimo edit binds 127.0.0.1 unless told otherwise. On a remote host that is invisible to an SSH-connected user, and answering "set up a LocalForward" spends their reconnect to buy what --host <tailnet-ip> gives for free. Verified: LISTEN 127.0.0.1:2718 before, HTTP 200 on the tailnet address after.
• Bind to the specific tailnet IP, never 0.0.0.0. With --no-token there is no auth at all, so the bind address is the access control — 0.0.0.0 exposes an executing kernel to every interface the box has.
• pkill -f 'marimo edit notebook.py' matches the shell running it, because -f sees the full command line including your own. Measured: it killed the launching shell and took the new server with it, leaving nothing listening and an empty log that reads like a startup failure. Record the PID at launch, or use pkill -x marimo / a [m]arimo character class.
• marimo export rewrites the source .py it exports. Measured with the server idle: one export html moved the notebook's mtime by 39 seconds without touching content. So exporting two formats leaves the first one older than the source — any "is the export newer than the source?" freshness check fails for whichever ran first. Export the format you will actually gate on last. Discovering this at the gate, after the work is done, is the expensive way to learn it.

Refreshing After a Data-Only Change

When you regenerate data a notebook reads, re-run every cell, not the ones you judge affected. Guessing which cells a data change touches is exactly what lets a stale render through — the dependency DAG cannot tell you, because no variable changed. For a thin-reader notebook (cells that just pl.read_parquet(...)) a full re-run is cheap.

bash ${CLAUDE_SKILL_DIR}/marimo-pair/scripts/execute-code.sh <<'EOF'
import marimo._code_mode as cm

async with cm.get_context() as ctx:
    for c in ctx.cells:
        ctx.run_cell(c.id)
EOF

Better still, wrap regenerate + refresh + export in one project script so the refresh cannot be lost by forgetting it — this project does, at scripts/repro/refresh.sh.

ctx.screenshot() is not a shortcut for confirming what the user sees: it is a coroutine (needs await, unlike every other ctx.* method) and it requires Playwright installed in the environment. Re-run the cells instead. Note cm.get_context() likewise needs async with, not with.

Discovery and Execution

# Discover running servers
bash ${CLAUDE_SKILL_DIR}/marimo-pair/scripts/discover-servers.sh

# Execute code in the kernel (one-liner)
bash ${CLAUDE_SKILL_DIR}/marimo-pair/scripts/execute-code.sh -c "df.head()"

# Execute code (multiline — use heredoc to avoid shell escaping)
bash ${CLAUDE_SKILL_DIR}/marimo-pair/scripts/execute-code.sh <<'EOF'
import marimo._code_mode as cm

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

Use --port to target a specific server, --session for a specific notebook, --url for remote servers.

Key Concepts

• Scratchpad execution: Code runs in the kernel with all cell variables in scope, but nothing persists between calls. Use this to explore and validate.
• Cell mutations: Use marimo._code_mode with async with cm.get_context() as ctx: to create/edit/delete cells and install packages. All ctx.* methods are synchronous — do NOT await them.
• Cells are not auto-executed: create_cell and edit_cell are structural only — call ctx.run_cell(cid) to execute.
• Install packages via ctx.install_packages(), not uv add or pip.
• NEVER write to the .py file directly while a session is running — the kernel owns it.

marimo-pair References

• marimo-pair/reference/finding-marimo.md — How to find and invoke the right marimo binary
• marimo-pair/reference/gotchas.md — Cached module proxies and other traps
• marimo-pair/reference/rich-representations.md — Custom anywidgets, _display_(), Arrow IPC for large data
• marimo-pair/reference/notebook-improvements.md — Setup cells, lifting functions, mo.persistent_cache

Data and Visualization

• Prefer polars over pandas for performance
• Use mo.ui for interactive widgets
• SQL cells: mo.sql(df, "SELECT * FROM df")
• Display markdown: mo.md("# Heading")

Debugging Workflow

1. Pre-execution validation:

# scripts: Validate notebook syntax and cell structure
scripts/check_notebook.sh notebook.py

Runs syntax check, marimo validation, and cell structure overview in one command.

2. Runtime errors: Export with outputs, then use notebook-debug skill:

# marimo: Export to ipynb with outputs for inspection
marimo export ipynb notebook.py -o __marimo__/notebook.ipynb --include-outputs

Common Issues

Issue	Fix
Variable redefinition	Rename one variable or merge cells
Circular dependency	Break cycle by merging or restructuring
Missing return	Add return var, with trailing comma
Import not available	Ensure import cell returns the module

Additional Resources

Reference Files

For detailed patterns and advanced techniques, consult:

• references/reactivity.md - DAG execution, variable rules, dependency detection patterns
• references/debugging.md - Error patterns, runtime debugging, environment-specific issues
• references/widgets.md - Interactive UI components and mo.ui patterns
• references/sql.md - SQL cells and database integration techniques
• marimo-pair/reference/finding-marimo.md - How to find and invoke marimo across project types
• marimo-pair/reference/gotchas.md - Cached module proxies (e.g., polars + pyarrow mid-session)
• marimo-pair/reference/rich-representations.md - Custom anywidgets, Arrow IPC, _display_() protocol
• marimo-pair/reference/notebook-improvements.md - Setup cells, lifting functions, mo.persistent_cache

Examples

Working examples available in examples/:

• examples/basic_notebook.py - Minimal marimo notebook structure
• examples/data_analysis.py - Data loading, filtering, and visualization patterns
• examples/interactive_widgets.py - Interactive UI component usage

Scripts

Validation and live-session utilities:

• scripts/check_notebook.sh - Primary validation: syntax check, marimo validation, cell structure overview
• scripts/get_cell_map.py - Extract cell metadata (invoked by check_notebook.sh)
• marimo-pair/scripts/discover-servers.sh - Find running marimo servers
• marimo-pair/scripts/execute-code.sh - Execute code in a running marimo kernel

Related Skills

• notebook-debug - Debugging executed ipynb files with tracebacks and output inspection
• marimo-pair/SKILL.md - Full marimo-pair protocol for live kernel interaction