feat: more sandbox variants

CHANGELOG.md

@@ -8,6 +8,26 @@
 
 ## Unreleased
 
+- Added the `kaggle` sandbox variant (`KaggleSandbox`) to connect to a Kaggle
+  interactive notebook runtime via `jupyter-kernel-client`'s
+  `KaggleKernelClient`. Authenticate with a Kaggle API token (`token` argument or
+  the `KAGGLE_API_TOKEN` environment variable) — omitting `kernel_id` then creates
+  a new kernel. Alternatively, connect to an existing session with a
+  `server_url`/`kernel_id` or a notebook session `channels_url` (the signed JWT in
+  the proxied URL provides the authentication). Install with
+  `pip install code-sandboxes[kaggle]`.
+- Enhanced `KaggleSandbox` with a transparent batch primitive: when no runtime
+  connection details are provided, it automatically executes code through
+  `KaggleKernelExecutor` (submit/poll/download) so integrations like
+  `jupyter-mcp-server` can run on Kaggle without requiring interactive runtime
+  wiring.
+- Added Kaggle accelerator forwarding in batch mode: `Sandbox.create(variant="kaggle", gpu=...)`
+  now passes the value to `KaggleKernelExecutor.execute(accelerator=...)`,
+  supporting both Kaggle API values (`NvidiaTeslaT4`, ...) and friendly aliases
+  (`T4`, `P100`, ...).
+- Updated `ColabSandbox` to be reuse-only for existing Colab runtimes and added
+  `channels_url` parsing support for extracting `server_url` / `kernel_id` /
+  `proxy_token` directly from the Colab WebSocket channels URL.
 - Breaking change: sandbox variant names are `eval`, `docker`, `jupyter`, and `datalayer`.
 - Removed support for the older `local-*` variant names from the public API and documentation.
 - Clarified in the documentation that `Sandbox.create()` defaults to `datalayer`.