feat(cccl): integrate missing CCCL directories — python/, ci/, .agent/, docs/, test/

Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream:

Added:
- python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms
  Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc.
  Includes 204 .py files with full test coverage for all 27 algorithms
- ci/ (163 files) — Build/test infrastructure
  build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml
  Directly maps to our [INFRA-CI] and [INFRA-BUILD] items
- .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL
  cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md
- docs/ (491 files) — Official CCCL documentation
  CI references, CMake guides, Python compute docs, libcudacxx PTX docs
- test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar)
- Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml
- CLAUDE.md symlink → AGENTS.md (NVIDIA's standard)

cccl_upstream now mirrors full NVIDIA/cccl structure:
  Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks)
  After:  53M (+python +ci +docs +.agent +test +configs)

This completes the CCCL base needed for:
- [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds
- [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations
- [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh
- Agent workflow: .agent/skills/ for consistent style and test patterns

cccl_upstream/docs/python/setup.rst

@@ -0,0 +1,106 @@
+.. _cccl-python-setup:
+
+Setup and Installation
+======================
+
+This guide walks you through installing and setting up the CUDA Python Core Libraries (CCCL).
+
+Prerequisites
+-------------
+
+Before installing cuda-cccl, ensure you have:
+
+* **Python 3.10 or later**
+* **CUDA Toolkit 12.x or 13.x**
+* **Compatible NVIDIA GPU** with Compute Capability 7.5 or higher
+* **Operating Systems:** Linux (tested on Ubuntu 20.04+) or Windows 10/11 (with WSL2 support)
+
+Installation
+------------
+
+Install from PyPI
+~~~~~~~~~~~~~~~~~
+
+The easiest way to install ``cuda-cccl`` is using pip:
+
+.. code-block:: bash
+
+   pip install cuda-cccl[cu13]  # or cuda-cccl[cu12]
+
+This will install ``cuda-cccl`` along with all required dependencies, including
+the ``cuda-toolkit`` pip packages for the chosen CUDA major version.
+
+If you already have a CUDA toolkit installed on your system (e.g., via the
+NVIDIA runfile, package manager, or Conda) and do not want pip to install it,
+use the ``sysctk`` variants instead:
+
+.. code-block:: bash
+
+   pip install cuda-cccl[sysctk13]  # or cuda-cccl[sysctk12]
+
+These install the same dependencies except ``cuda-toolkit``; it is your
+responsibility to ensure a compatible CUDA toolkit is on ``PATH`` and
+``LD_LIBRARY_PATH``.
+
+For a minimal install without Numba (useful when you supply your own
+:ref:`pre-compiled operators <cuda.compute.externally_compiled_operators>`), use:
+
+.. code-block:: bash
+
+   pip install cuda-cccl[minimal-cu13]      # pip-installed CUDA toolkit
+   pip install cuda-cccl[minimal-sysctk13]  # system CUDA toolkit
+
+Free-threaded Python support is currently validated with the ``minimal-cu12``
+and ``minimal-cu13`` extras. The full ``cu12`` and ``cu13`` extras depend on
+Numba CUDA and are not currently supported in free-threaded Python.
+
+Install from conda-forge
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Alternatively, you can install ``cuda-cccl`` using conda:
+
+.. code-block:: bash
+
+   conda install -c conda-forge cccl-python
+
+This will install the CCCL Python libraries and their dependencies from the conda-forge channel.
+
+Install from Source
+~~~~~~~~~~~~~~~~~~~
+
+For development or to access the latest features:
+
+.. code-block:: bash
+
+   git clone https://github.com/NVIDIA/cccl.git
+   cd cccl/python/cuda_cccl
+   pip install -e .[test-cu13]  # or .[test-cu12], .[test-sysctk13], .[test-sysctk12]
+
+The test extras do not install CuPy. To also run the CuPy-based
+``cuda.compute`` examples, install CuPy separately, for example
+``pip install cupy-cuda13x``.
+
+
+Development Setup
+~~~~~~~~~~~~~~~~~~
+
+For contributing to cuda-cccl or advanced development:
+
+.. code-block:: bash
+
+   # Clone the repository
+   git clone https://github.com/NVIDIA/cccl.git
+   cd cccl/python/cuda_cccl
+
+   # Install in development mode with test dependencies
+   pip install -e .[test-cu13]  # or .[test-cu12], .[test-sysctk13], .[test-sysctk12]
+
+   # Run tests to verify everything works
+   pytest tests/
+
+Next Steps
+----------
+
+Now that you have ``cuda-cccl`` installed, check out:
+
+* :doc:`compute/index` - Parallel computing primitives for operations on arrays or data ranges