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/libcudacxx/extended_api/math/sincos.rst

@@ -0,0 +1,68 @@
+.. _libcudacxx-extended-api-math-sincos:
+
+``cuda::sincos``
+====================================
+
+Defined in the ``<cuda/cmath>`` header.
+
+.. code:: cuda
+
+   namespace cuda {
+
+   template <class T>
+   struct sincos_result
+   {
+     T sin;
+     T cos;
+   };
+
+   template </*floating-point-type*/ T>
+   [[nodiscard]] __host__ __device__
+   sincos_result<T> sincos(T value) noexcept; // (1)
+
+   template <class Integral>
+   [[nodiscard]] __host__ __device__
+   sincos_result<double> sincos(Integral value) noexcept; // (2)
+
+   } // namespace cuda
+
+Computes :math:`\sin value` and :math:`\cos value` at the same time using more efficient algorithms than if operations were computed separately.
+
+**Parameters**
+
+- ``value``: The input value.
+
+**Return value**
+
+- ``cuda::sincos_result`` object with both values set to ``NaN`` if the input value is :math:`\pm\infty` or ``NaN`` and to results of :math:`\sin value` and :math:`\cos value` otherwise. (1)
+- if ``T`` is an integral type, the input value is treated as ``double``. (2)
+
+**Constraints**
+
+- ``T`` is an arithmetic type.
+
+**Performance considerations**
+
+- If available, the functionality is implemented by compiler builtins, otherwise fallbacks to ``cuda::std::sin(value)`` and ``cuda::std::cos(value)``.
+
+Example
+-------
+
+.. code:: cuda
+
+    #include <cuda/cmath>
+    #include <cuda/std/cassert>
+
+    __global__ void sincos_kernel() {
+        auto [sin_pi, cos_pi] = cuda::sincos(0.f);
+        assert(sin_pi == 0.f);
+        assert(cos_pi == 1.f);
+    }
+
+    int main() {
+        sincos_kernel<<<1, 1>>>();
+        cudaDeviceSynchronize();
+        return 0;
+    }
+
+`See it on Godbolt 🔗 <https://godbolt.org/z/99PP9s1z6>`__