README v2: sample provenance + quickstart notebook

examples/quickstart.ipynb

@@ -4,7 +4,15 @@
    "cell_type": "markdown",
    "id": "intro",
    "metadata": {},
-   "source": "# CounterStrike-1K — Quickstart\n\nThis notebook runs end-to-end against the public preview repo. No environment variables, no config files. Open and run all.\n\n**What you'll see**: 1) browse the manifest, 2) decode one sample, 3) inspect actions/state, 4) watch the video, 5) **HUD overlay to verify action–video alignment**, 6) load all 10 synchronized POVs of one round.\n\n**Setup** (run once in a fresh project directory):\n\n```bash\nmkdir cs1k-demo && cd cs1k-demo\nuv init\nuv add datasets \"counterstrike1k @ git+https://github.com/AnirudhhRamesh/counterstrike1k\" jupyterlab matplotlib pandas\n```\n\nOr with pip:\n\n```bash\nmkdir cs1k-demo && cd cs1k-demo\npython -m venv .venv && source .venv/bin/activate\npip install datasets \"counterstrike1k @ git+https://github.com/AnirudhhRamesh/counterstrike1k\" jupyterlab matplotlib pandas\n```"
+   "source": "# CounterStrike-1K — Quickstart\n\nEnd-to-end walkthrough of the public preview dataset. **Open in Colab and run all** — the first cell installs the loader.\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AnirudhhRamesh/CounterStrike-1K/blob/main/cs2_release/quickstart.ipynb)\n\n**What you'll see**: 1) browse the manifest, 2) decode one sample, 3) inspect actions/state, 4) watch the video, 5) **HUD overlay to verify action–video alignment**, 6) load all 10 synchronized POVs of one round.\n\nIf you're running locally instead of Colab, `uv add datasets \"counterstrike1k @ git+https://github.com/AnirudhhRamesh/counterstrike1k\"` once and skip the next cell."
+  },
+  {
+   "cell_type": "code",
+   "id": "2d22fcd3",
+   "source": "# Colab / fresh-kernel install. Local users with `uv add` already done can skip this cell.\n%pip install -q \"counterstrike1k @ git+https://github.com/AnirudhhRamesh/counterstrike1k\" datasets pandas matplotlib pillow av",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",