add a rapids-singlecell analysis skill

README.md

@@ -16,6 +16,22 @@ Computations use CuPy and NVIDIA RAPIDS for performance on large datasets.
 - **GPU acceleration**: Common single-cell workflows on `AnnData` run on the GPU.
 - **Ecosystem compatibility**: Works with Scanpy APIs; includes pieces from Squidpy, decoupler, and pertpy.
 - **Simple installation**: Available via Conda and PyPI.
+- **Version-matched agent skill**: Released pip packages include an analysis skill for Codex, Claude, and other agents.
+
+## Agent skill
+
+Released packages include the matching model-neutral analysis skill:
+
+```bash
+rapids-singlecell-install-skills --agent codex
+rapids-singlecell-install-skills --agent claude
+rapids-singlecell-install-skills --agent claude-science  # or: --agent agents
+rapids-singlecell-check-kernel                    # before starting Jupyter
+```
+
+Use `--dest /path/to/skills/rapids-singlecell` for another agent. See the
+[installation guide](https://rapids-singlecell.readthedocs.io/en/latest/installation.html#agent-skill)
+for installation and preflight details.
 
 ## Documentation
 

docs/installation.md

@@ -166,6 +166,51 @@ Use multiple architectures when building portable binaries (e.g., for a shared c
 The `-real` suffix generates device code only (no PTX fallback), which reduces binary size.
 ```
 
+## Agent skill
+
+Released packages contain a matching model-neutral analysis skill. Copy it to
+an agent's personal skill directory after installing RSC:
+
+```bash
+rapids-singlecell-install-skills --agent codex
+rapids-singlecell-install-skills --agent claude
+rapids-singlecell-install-skills --agent claude-science
+rapids-singlecell-install-skills --agent agents
+rapids-singlecell-install-skills --dest /other/skills/rapids-singlecell
+```
+
+`--check` compares the installed copy with the bundle in the active package;
+`--force` replaces a differing copy. `--print-path` prints the bundled source.
+For Claude Science, the installer resolves the active organization from
+`~/.claude-science/active-org.json`.
+
+```bash
+rapids-singlecell-install-skills --check --agent codex
+```
+
+Before starting Jupyter, verify RMM, GPU/CUDA execution, the RSC import, native
+extensions, and a representative RSC kernel:
+
+```bash
+rapids-singlecell-check-kernel
+rapids-singlecell-check-kernel --mode managed  # planned oversubscription
+```
+
+The preflight is disposable; configure RMM again at the start of the notebook.
+
+### Bootstrap from a standalone skill
+
+If RSC is not installed, create a fresh environment from the matching canonical
+definition on `main`:
+
+- [CUDA 12](https://github.com/scverse/rapids_singlecell/blob/main/conda/rsc_rapids_26.04_cuda12.yml)
+- [CUDA 13](https://github.com/scverse/rapids_singlecell/blob/main/conda/rsc_rapids_26.04_cuda13.yml)
+
+```bash
+CONDA_CHANNEL_PRIORITY=flexible mamba env create --name rsc \
+  --file /path/to/rsc_rapids_26.04_cuda13.yml
+```
+
 ## Docker
 
 We also offer Docker containers for `rapids-singlecell`. These containers include all the necessary dependencies, making it even easier to get started with `rapids-singlecell`.

pyproject.toml

@@ -15,6 +15,8 @@ license = { file = "LICENSE" }
 authors = [ { name = "Severin Dicks" } ]
 readme = { file = "README.md", content-type = "text/markdown" }
 dynamic = [ "version" ]
+scripts.rapids-singlecell-install-skills = "rapids_singlecell_skills.install:main"
+scripts.rapids-singlecell-check-kernel = "rapids_singlecell_skills.kernel:main"
 
 dependencies = [
     "anndata>=0.10.0",
@@ -152,7 +154,7 @@ local_scheme = "node-and-date"
 version-file = "src/rapids_singlecell/_version.py"
 
 [tool.hatch.build.targets.wheel]
-packages = [ "src/rapids_singlecell", "src/testing" ]
+packages = [ "src/rapids_singlecell", "src/rapids_singlecell_skills", "src/testing" ]
 exclude = [ "**/*.cu", "**/*.cuh", "**/*.h" ]
 
 [tool.hatch.build.targets.wheel.hooks.scikit-build]

src/rapids_singlecell/__init__.py

@@ -5,6 +5,8 @@
 from . import dcg, get, gr, pp, ptg, tl
 from ._version import __version__
 
+__all__ = ["dcg", "get", "gr", "pp", "ptg", "tl"]
+
 
 def _detect_duplicate_installation():
     """Warn if multiple rapids_singlecell variants are installed."""

src/rapids_singlecell/dcg.py

@@ -1,3 +1,5 @@
 from __future__ import annotations
 
-from .decoupler_gpu import *
+from .decoupler_gpu import aucell, mlm, ulm, waggr, zscore
+
+__all__ = ["aucell", "mlm", "ulm", "waggr", "zscore"]

src/rapids_singlecell/get/__init__.py

@@ -8,3 +8,5 @@
 # Aggregation imports preprocessing modules that use the names exported above.
 # isort: split
 from ._aggregated import aggregate
+
+__all__ = ["X_to_CPU", "X_to_GPU", "aggregate", "anndata_to_CPU", "anndata_to_GPU"]

src/rapids_singlecell/gr.py

@@ -1,3 +1,5 @@
 from __future__ import annotations
 
-from .squidpy_gpu import *
+from .squidpy_gpu import calculate_niche, co_occurrence, ligrec, spatial_autocorr
+
+__all__ = ["calculate_niche", "co_occurrence", "ligrec", "spatial_autocorr"]

src/rapids_singlecell/pp.py

@@ -1,3 +1,50 @@
 from __future__ import annotations
 
-from .preprocessing import *
+from .preprocessing import (
+    bbknn,
+    calculate_qc_metrics,
+    filter_cells,
+    filter_genes,
+    harmony_integrate,
+    highly_variable_genes,
+    log1p,
+    neighbors,
+    normalize_pearson_residuals,
+    normalize_total,
+    pca,
+    regress_out,
+    scale,
+    scrublet,
+    scrublet_simulate_doublets,
+    sqrt,
+)
+from .preprocessing import (
+    filter_highly_variable as filter_highly_variable,
+)
+from .preprocessing import (
+    flag_gene_family as flag_gene_family,
+)
+
+__all__ = [
+    "bbknn",
+    "calculate_qc_metrics",
+    "filter_cells",
+    "filter_genes",
+    "harmony_integrate",
+    "highly_variable_genes",
+    "log1p",
+    "neighbors",
+    "normalize_pearson_residuals",
+    "normalize_total",
+    "pca",
+    "regress_out",
+    "scale",
+    "scrublet",
+    "scrublet_simulate_doublets",
+    "sqrt",
+]
+
+__deprecated_exports__ = {
+    "filter_highly_variable": "Deprecated; do not use in new analyses.",
+    "flag_gene_family": "Deprecated; do not use in new analyses.",
+}

src/rapids_singlecell/preprocessing/_neighbors/__init__.py

@@ -279,6 +279,8 @@ def bbknn(
         of a symmetrical matrix of connectivities.
     n_pcs
         Use this many PCs. If `n_pcs==0` and `use_rep is None`, use `.X`.
+    batch_key
+        Key in `adata.obs` containing the batch labels to balance. Required.
     use_rep
         Use the indicated representation. `'X'` or any key for `.obsm` is valid.
         If `None`, the representation is chosen automatically: For `.n_vars < 50`, `.X`

src/rapids_singlecell/preprocessing/_qc.py

@@ -26,7 +26,7 @@ def calculate_qc_metrics(
     layer: str = None,
 ) -> None:
     """\
-    Calculates basic qc Parameters :cite:p:`McCarthy2017`.
+    Calculate basic quality-control metrics :cite:p:`McCarthy2017`.
 
     Calculates number of genes per cell (n_genes) and number of counts per cell (n_counts).
     Loosely based on calculate_qc_metrics from scanpy :cite:p:`Wolf2018`. Updates :attr:`~anndata.AnnData.obs` and :attr:`~anndata.AnnData.var`  with columns with qc data.

src/rapids_singlecell/ptg.py

@@ -1,3 +1,37 @@
 from __future__ import annotations
 
-from .pertpy_gpu import *
+from .pertpy_gpu import (
+    Distance,
+    GuideAssignment,
+    Mixscale,
+    Mixscape,
+)
+from .pertpy_gpu import (
+    MeanVar as MeanVar,
+)
+
+__all__ = ["Distance", "GuideAssignment", "Mixscale", "Mixscape"]
+
+__deprecated_exports__ = {
+    "MeanVar": "Deprecated; do not use in new analyses.",
+}
+
+# Public class members are part of the installed API contract used by agent tooling.
+# Keep these in sync with docs/api/pertpy_gpu.md.
+__api_members__ = {
+    "Distance": [
+        "__call__",
+        "pairwise",
+        "onesided_distances",
+        "contrast_distances",
+        "create_contrasts",
+        "bootstrap",
+    ],
+    "GuideAssignment": [
+        "assign_by_threshold",
+        "assign_to_max_guide",
+        "assign_mixture_model",
+    ],
+    "Mixscale": ["perturbation_signature", "mixscale"],
+    "Mixscape": ["perturbation_signature", "mixscape", "lda"],
+}

src/rapids_singlecell/tl.py

@@ -1,3 +1,42 @@
 from __future__ import annotations
 
-from .tools import *
+from .tools import (
+    diffmap,
+    draw_graph,
+    embedding_density,
+    ingest,
+    leiden,
+    louvain,
+    pca,
+    rank_genes_groups,
+    score_genes,
+    score_genes_cell_cycle,
+    tsne,
+    umap,
+)
+from .tools import (
+    kmeans as kmeans,
+)
+from .tools import (
+    rank_genes_groups_logreg as rank_genes_groups_logreg,
+)
+
+__all__ = [
+    "diffmap",
+    "draw_graph",
+    "embedding_density",
+    "ingest",
+    "leiden",
+    "louvain",
+    "pca",
+    "rank_genes_groups",
+    "score_genes",
+    "score_genes_cell_cycle",
+    "tsne",
+    "umap",
+]
+
+__deprecated_exports__ = {
+    "kmeans": "Deprecated; do not use in new analyses.",
+    "rank_genes_groups_logreg": rank_genes_groups_logreg.__deprecated__,
+}

src/rapids_singlecell_skills/__init__.py

@@ -0,0 +1 @@
+"""Agent-skill tools bundled with RAPIDS-singlecell."""