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/thrust/index.rst

@@ -0,0 +1,114 @@
+.. _thrust-module:
+
+Thrust
+======
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+
+   Overview <self>
+   algorithms
+   containers
+   function_objects
+   iterators
+   memory_management
+   numerics
+   parallel_execution_policies
+   random
+   system
+   utility
+   API reference <api/index>
+   developer_overview
+
+Thrust is the C++ parallel algorithms library which inspired the introduction of parallel algorithms to the
+C++ Standard Library. Thrust's **high-level** interface greatly enhances programmer **productivity** while
+enabling performance portability between GPUs and multicore CPUs.
+It builds on top of established parallel programming frameworks (such as CUDA, TBB, and OpenMP).
+It also provides a number of general-purpose facilities similar to those found in the C++ Standard Library.
+
+Thrust is an open source project; it is available on `GitHub <https://github.com/NVIDIA/cccl>`__
+and included in the NVIDIA HPC SDK and CUDA Toolkit.
+If you have one of those SDKs installed, no additional installation or compiler flags are needed to use Thrust.
+
+Examples
+--------
+
+Thrust is best learned through examples.
+
+-------------
+CMake Example
+-------------
+
+A complete, standalone example project showing how to write a CMake build system that uses Thrust with any supported
+device system is available in the CCCL repository `here <https://github.com/NVIDIA/cccl/tree/main/examples/thrust_flexible_device_system>`__.
+
+------------------
+Thrust API Example
+------------------
+
+The following example generates random numbers serially and then transfers them
+  to a parallel device where they are sorted.
+
+.. code:: cpp
+
+   #include <thrust/host_vector.h>
+   #include <thrust/device_vector.h>
+   #include <thrust/generate.h>
+   #include <thrust/sort.h>
+   #include <thrust/copy.h>
+   #include <thrust/random.h>
+
+   int main() {
+   // Generate 32M random numbers serially.
+   thrust::default_random_engine rng(1337);
+   thrust::uniform_int_distribution<int> dist;
+   thrust::host_vector<int> h_vec(32 << 20);
+   thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });
+
+   // Transfer data to the device.
+   thrust::device_vector<int> d_vec = h_vec;
+
+   // Sort data on the device.
+   thrust::sort(d_vec.begin(), d_vec.end());
+
+   // Transfer data back to host.
+   thrust::copy(d_vec.begin(), d_vec.end(), h_vec.begin());
+   }
+
+
+`See it on Godbolt <https://godbolt.org/z/GeWEd8Er9>`__
+
+This example demonstrates computing the sum of some random numbers in parallel:
+
+.. code:: cpp
+
+   #include <thrust/host_vector.h>
+   #include <thrust/device_vector.h>
+   #include <thrust/generate.h>
+   #include <thrust/reduce.h>
+   #include <thrust/functional.h>
+   #include <thrust/random.h>
+
+   int main() {
+   // Generate random data serially.
+   thrust::default_random_engine rng(1337);
+   thrust::uniform_real_distribution<double> dist(-50.0, 50.0);
+   thrust::host_vector<double> h_vec(32 << 20);
+   thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });
+
+   // Transfer to device and compute the sum.
+   thrust::device_vector<double> d_vec = h_vec;
+   double x = thrust::reduce(d_vec.begin(), d_vec.end(), 0, thrust::plus<int>());
+   }
+
+`See it on Godbolt <https://godbolt.org/z/cnsbWWME7>`__
+
+Getting The Thrust Source Code & Developing Thrust
+-----------------------------------------------------
+
+Thrust started as a stand-alone project, but as of March 2024 Thrust is a part of the
+CUDA Core Compute Libraries (CCCL). Please refer to the
+`CCCL Getting Started section <https://github.com/NVIDIA/cccl?tab=readme-ov-file#getting-started>`__ and the
+`Contributing Guide: <https://github.com/NVIDIA/cccl/blob/main/CONTRIBUTING.md>`__ for instructions on how to
+get started developing the CCCL sources.