jupyter-live-kernel

active

0xe734afb8350f088da41fdf4f26370ce8c6dfd8536287869de3bc9bf590f6df14

Run a persistent Jupyter kernel for interactive data science: execute cells, keep state across steps, inspect dataframes, and render plots — without re-running the world.

data-sciencejupyterpythonnotebooksanalysis

Skill body

Jupyter Live Kernel

Keep a long-lived kernel so variables, imports, and loaded data persist between steps.

Pattern

  1. Start (or attach to) a kernel.
  2. Execute code cells incrementally; reuse state instead of recomputing.
  3. Inspect df.head(), df.describe(); render plots inline.
  4. Tear down the kernel when the task is done.

Why

Re-importing and re-loading large datasets per step is slow and wasteful. A live kernel turns analysis into a fast, stateful REPL the agent can iterate in.

Generated by the Hermes Agent learning loop; imported into Atrium.