From nightly
Use nteract notebooks as a persistent Python REPL. Trigger this skill whenever you're about to run python3 -c, write a throwaway .py script, or chain multiple shell commands for data exploration, analysis, plotting, or iterative computation. Notebooks preserve state between cells, show rich output, and can be used in realtime with users.
npx claudepluginhub nteract/claude-plugin --plugin nightlyThis skill uses the workspace's default tool permissions.
When you have nteract MCP tools available and you're about to do multi-step Python work — chaining `python3 -c` commands, writing a throwaway `.py` script, or running exploratory code — use a notebook instead. You get persistent state between cells, rich output (tables, plots, errors with tracebacks), and users and agents can view the notebook in realtime.
Guides Next.js Cache Components and Partial Prerendering (PPR): 'use cache' directives, cacheLife(), cacheTag(), revalidateTag() for caching, invalidation, static/dynamic optimization. Auto-activates on cacheComponents: true.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
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When you have nteract MCP tools available and you're about to do multi-step Python work — chaining python3 -c commands, writing a throwaway .py script, or running exploratory code — use a notebook instead. You get persistent state between cells, rich output (tables, plots, errors with tracebacks), and users and agents can view the notebook in realtime.
If you loaded this skill but tools like create_notebook, create_cell, execute_cell are not available, don't fall back to python3 -c. Ask the user to run /reload-plugins and try again. The most common cause is that the plugin was installed this session — Claude Code queues newly-registered MCP servers for the next reload.
If tools still don't appear after /reload-plugins:
runtimed via a Unix socket).runt doctor to check on the installation. (runt-nightly if this is the nightly release)create_notebook(path="~/analysis.ipynb")
create_cell(source="import pandas as pd\ndf = pd.read_csv('data.csv')\ndf.head()", cell_type="code", and_run=true)
Start a notebook:
create_notebook(path="~/analysis.ipynb") — creates and opens it.
Add and run code cells:
create_cell(source="your code here", cell_type="code", and_run=true) — creates the cell AND executes it in one call. State persists: variables from earlier cells are available in later ones.
Iterate on a cell:
set_cell(cell_id="...", source="updated code") then execute_cell(cell_id="...") — edit and re-run without creating a new cell.
Check your work:
get_all_cells(format="summary", include_outputs=true) — see all cells with output previews at a glance.
5a. Save when done:
save_notebook() — writes the .ipynb to disk.
5b. Open the App for the User:
show_notebook() — opens the notebook app for the user. Use if they ask to show it to them. Can be disruptive if the user doesn't expect it.
python3 -c commandspython3 -c "print(2+2)" is fine as-is)python3 script.py)