diff --git a/README.md b/README.md
index 16ec7af..234d38f 100644
--- a/README.md
+++ b/README.md
@@ -51,9 +51,10 @@ cpm = cau.counts_per_million(counts)
 | `cau.accession` | Accession ids for SpCas9 guides, encoded from the guide's own sequence |
 | `cau.counts_per_million` | Counts-per-million normalization of count matrices |
 | `cau.guide_alignment` | Where a guide library maps: each guide's exact sites, class, coordinates and target site (also the `cau guide-alignment` command) |
-| `cau.guide_annotation` | What those sites hit: exon, intron, promoter or intergenic, the gene and transcript, and the distance to the nearest TSS (also the `cau guide-annotation` command) |
+| `cau.guide_annotation` | What those sites hit: genic or intergenic, exon or intron, every promoter they sit in, the gene and transcript, and the distances to the nearest TSS and the gene's 5' end (also the `cau guide-annotation` command) |
 | `cau.element_mapping` | Which element of a screen each guide tests, and which guides are controls (also the `cau element-mapping` command) |
 | `cau.igvf_export` | The guide design in the IGVF per-guide metadata format, ready to submit or to feed the CRISPR pipeline (also the `cau igvf-export` command) |
+| `cau.mudata_builder` | A sequenced channel's gene and guide counts as one MuData, annotated and laid out like the IGVF pipeline's object (also the `cau mudata-builder` command) |
 
 The `cau` command line tool comes with the package:
 
diff --git a/docs/mudata-builder.md b/docs/mudata-builder.md
new file mode 100644
index 0000000..bc9c735
--- /dev/null
+++ b/docs/mudata-builder.md
@@ -0,0 +1,102 @@
+# MuData builder
+
+`cau mudata-builder` assembles a sequenced channel's two count matrices into
+one object. The four steps before it describe a library on paper;
+[IGVF export](igvf-export.md) ends at `guide_metadata.tsv`. This is where the
+design meets the data.
+
+In Claude Code the `cau-mudata-builder` skill teaches Claude to run it and
+read the output ([Agents and skills](agents-and-skills.md)).
+
+## Quick start
+
+```bash
+cau mudata-builder run \
+    --rna counts_unfiltered/adata.h5ad \
+    --guide counts_unfiltered_modified/adata.h5ad \
+    --guide-metadata results/igvf/guide_metadata.tsv \
+    --store gencode.v43.annotation.parquet \
+    --measurement-set IGVFDS8842PXCC \
+    --gtf-accession IGVFFI9561BASO \
+    --outdir results/
+```
+
+Nothing is written outside `--outdir`.
+
+## What it assembles
+
+| Where | What |
+| --- | --- |
+| `mod['gene'].X` | the gene counts, untouched, with the kb layers beside them |
+| `mod['gene'].var` | `symbol`, `gene_type`, `gene_chr`, `gene_start`, `gene_end` |
+| `mod['guide'].X` | the guide counts, untouched |
+| `mod['guide'].var` | every column of `guide_metadata.tsv` |
+| `obs` | `barcode`, `measurement_set`, and any `--covariates` given |
+| `obsm['gene']`, `obsm['guide']` | which modality holds each cell |
+| `uns['reference']` | the GTF and genome: name, md5 and accession |
+
+The layout follows the IGVF CRISPR pipeline's own object, so the two can be
+compared field by field.
+
+## It assembles, and nothing else
+
+No per-cell or per-feature statistics, no QC, no filtering, no doublet calls,
+no guide assignment, no pairs to test. Every one of those is a judgement, and
+a step that makes judgements while assembling hides them. They belong to the
+steps after this one, where they can be seen and argued with.
+
+## Cells are the union
+
+A barcode seen in one modality and not the other is kept, and `obsm` records
+which modality holds it. On one real channel that is 1,022,982 barcodes
+against 207,274 seen by both: an intersection would discard four fifths of
+the object before anyone had looked at it. Filtering comes later, once
+somebody has decided what to filter on.
+
+## Every gene type is annotated
+
+The counts come from a transcriptome built on the whole GTF, so two thirds of
+the features are not protein-coding:
+
+```text
+protein_coding           20,066
+lncRNA                   18,874
+processed_pseudogene     10,150
+unprocessed_pseudogene    2,608
+misc_RNA                  2,221
+snRNA                     1,910
+...                      62,757 in total
+```
+
+Annotating only the coding genes would leave 42,691 features nameless, so the
+store is read with every gene type it holds, not only the ones guides are
+annotated against.
+
+## Two traps
+
+**PAR genes appear twice.** 47 features carry a `_PAR_Y` suffix and have a
+chrX twin with identical sequence, so kallisto cannot tell their reads apart
+and counts them as two features. Both rows are carried through untouched.
+Anything downstream that sums or de-duplicates them is making a choice; this
+step does not.
+
+**The ids carry GENCODE versions.** kb indexes genes as `ENSG00000290825.1`
+while the store drops the version, and the two are joined on the store's own
+rule: strip a trailing `.N`, leave `_PAR_Y` alone. A rule that split on the
+first dot instead would hand the chrY copy the chrX gene's coordinates, and
+nothing would complain.
+
+## One GENCODE, checked
+
+Genes and guides must be annotated against the same release, or the two
+halves of the object describe different genomes. The run reads the record
+beside `guide_metadata.tsv` and refuses a store other than the one that wrote
+it. Where no record is found it says so rather than assuming.
+
+`uns['reference']` then carries the GTF and the genome by **name, checksum
+and accession**, so the object answers "which GENCODE, which genome" on its
+own. A path is a claim; a checksum is evidence. Accessions come from
+`--gtf-accession` and `--genome-accession` -- the CRISPR pipeline's inputs
+carry the IGVF portal ids -- and fall back to the file's name, so the field
+is never empty and never a guess. Only the name is stored, never the path:
+the object is shared, and a path carries the filesystem it was built on.
diff --git a/mkdocs.yml b/mkdocs.yml
index cafb266..f004183 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -50,6 +50,7 @@ nav:
       - Gene annotation: guide-annotation.md
       - Element mapping: element-mapping.md
       - IGVF export: igvf-export.md
+      - MuData builder: mudata-builder.md
   - Agents and skills: agents-and-skills.md
   - API reference:
       - Overview: reference/index.md
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+  - ipywidgets ; extra == 'jupyter'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/9c/01/135abc57ed3e5dc5325411d8603457e41965ccaf4fcdec5d62b810a25ead/gtfreader-0.3.0.tar.gz
   name: gtfreader
   version: 0.3.0
@@ -2062,6 +2558,29 @@ packages:
   - numpy
   - pyarrow ; extra == 'fast-io'
   requires_python: '>=3.12'
+- pypi: https://files.pythonhosted.org/packages/9d/96/b3bf9a31978d936654a73f2bb1036b92b6515164f170092d162419eb771c/numcodecs-0.17.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
+  name: numcodecs
+  version: 0.17.0
+  sha256: 6c3342d91ed7cf59c1be84396edd364e936bb0ec9e366d24bb69689748d19625
+  requires_dist:
+  - numpy>=2.0
+  - typing-extensions
+  - msgpack ; extra == 'msgpack'
+  - zfpy>=1.0.0 ; extra == 'zfpy'
+  - pcodec>=1,<2 ; extra == 'pcodec'
+  - crc32c>=2.7 ; extra == 'crc32c'
+  - google-crc32c>=1.5 ; extra == 'google-crc32c'
+  - sphinx ; extra == 'docs'
+  - sphinx-issues ; extra == 'docs'
+  - pydata-sphinx-theme ; extra == 'docs'
+  - numpydoc ; extra == 'docs'
+  - myst-parser ; extra == 'docs'
+  - coverage ; extra == 'test'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pyzstd ; extra == 'test'
+  - importlib-metadata ; extra == 'test-extras'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/a0/f4/c67b0b3f1b9245e8d266f0f112c500d50e5b4e83cb6f3b71b6528104182a/requests-2.34.2-py3-none-any.whl
   name: requests
   version: 2.34.2
@@ -2074,6 +2593,25 @@ packages:
   - pysocks>=1.5.6,!=1.5.7 ; extra == 'socks'
   - chardet>=3.0.2,<8 ; extra == 'use-chardet-on-py3'
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/a6/82/0dbc9bc77b49cfb9268dc2b2b1dcd04346c8c7ed52e272b76a628a941656/zarr-3.4.1-py3-none-any.whl
+  name: zarr
+  version: 3.4.1
+  sha256: 38b540578a119352bdce02a720d7bfd99846e77f0728c58b1bab3d1f547526a0
+  requires_dist:
+  - donfig>=0.8
+  - google-crc32c>=1.5
+  - msgspec>=0.19
+  - numcodecs>=0.16
+  - numpy>=2
+  - packaging>=22.0
+  - typing-extensions>=4.14
+  - cast-value-rs>=0.4.2 ; extra == 'cast-value-rs'
+  - typer ; extra == 'cli'
+  - cupy-cuda12x ; sys_platform != 'darwin' and extra == 'gpu'
+  - universal-pathlib ; extra == 'optional'
+  - fsspec>=2023.10.0 ; extra == 'remote'
+  - obstore>=0.5.1 ; extra == 'remote'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/ad/21/17c1bc9e6f47c972ad66fb2ac2568f99f90f1207eeb6fc3b34d094dba7b5/mkdocs_material-9.7.7-py3-none-any.whl
   name: mkdocs-material
   version: 9.7.7
@@ -2098,6 +2636,25 @@ packages:
   - mkdocs-redirects>=1.2 ; extra == 'recommended'
   - mkdocs-rss-plugin>=1.6 ; extra == 'recommended'
   requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/ae/57/0e237d7091d2cd44a35d243b7344227f90440166860469cd036afc23a04d/pydantic_core-2.50.1-cp313-cp313-macosx_10_12_x86_64.whl
+  name: pydantic-core
+  version: 2.50.1
+  sha256: d5e062c01286d861fd6a1c4ff6e063547b3e713067f2df033c0ff97ac2ca006b
+  requires_dist:
+  - typing-extensions>=4.16.0
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/af/25/3e8e567bd48448e225ea27318ccf2b94e05124e7b8b97b13eaec9e127199/google_crc32c-1.9.0-cp313-cp313-macosx_12_0_x86_64.whl
+  name: google-crc32c
+  version: 1.9.0
+  sha256: f894a2877650b56201d26a012a257b76d54a68834dc3913a93830ca8a047b075
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/b0/65/5e088a45d0f43cd814bc5bec521c051d42005a472e804b1a36c48dada09b/h5py-3.16.0-cp313-cp313-macosx_11_0_arm64.whl
+  name: h5py
+  version: 3.16.0
+  sha256: 42108e93326c50c2810025aade9eac9d6827524cdccc7d4b75a546e5ab308edb
+  requires_dist:
+  - numpy>=1.21.2
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl
   name: pyyaml
   version: 6.0.3
@@ -2110,11 +2667,64 @@ packages:
   requires_dist:
   - pyyaml>=3.10 ; extra == 'watchmedo'
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/b6/55/4540ee0f9c42a9ad7109d0d1a8cc70de54c3572b01c6693a2b1c70e90ceb/scipy-1.18.1-cp313-cp313-macosx_10_15_x86_64.whl
+  name: scipy
+  version: 1.18.1
+  sha256: 3ab3523da44749156e1f68b464dc56af11ae4cbc5c739a49d05f32b982eca9f3
+  requires_dist:
+  - numpy>=2.0.0,<2.8
+  - pytest>=8.0.0 ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-timeout ; extra == 'test'
+  - pytest-xdist ; extra == 'test'
+  - asv ; extra == 'test'
+  - mpmath ; extra == 'test'
+  - gmpy2 ; extra == 'test'
+  - threadpoolctl ; extra == 'test'
+  - scikit-umfpack ; extra == 'test'
+  - pooch ; extra == 'test'
+  - hypothesis>=6.30 ; extra == 'test'
+  - array-api-strict>=2.3.1 ; extra == 'test'
+  - cython ; extra == 'test'
+  - meson ; extra == 'test'
+  - ninja ; sys_platform != 'emscripten' and extra == 'test'
+  - scipy-doctest>=2.0.0 ; extra == 'test'
+  - sphinx>=5.0.0,<8.2.0 ; extra == 'doc'
+  - intersphinx-registry ; extra == 'doc'
+  - pydata-sphinx-theme>=0.15.2 ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - sphinx-design>=0.4.0 ; extra == 'doc'
+  - matplotlib>=3.5 ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - jupytext ; extra == 'doc'
+  - myst-nb>=1.2.0 ; extra == 'doc'
+  - pooch ; extra == 'doc'
+  - jupyterlite-sphinx>=0.19.1 ; extra == 'doc'
+  - jupyterlite-pyodide-kernel ; extra == 'doc'
+  - linkify-it-py ; extra == 'doc'
+  - tabulate ; extra == 'doc'
+  - click<8.3.0 ; extra == 'dev'
+  - spin ; extra == 'dev'
+  - mypy==1.19.1 ; extra == 'dev'
+  - pyrefly==0.63.0 ; extra == 'dev'
+  - typing-extensions ; extra == 'dev'
+  - types-psutil ; extra == 'dev'
+  - pycodestyle ; extra == 'dev'
+  - ruff>=0.12.0 ; extra == 'dev'
+  - cython-lint>=0.12.2 ; extra == 'dev'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl
   name: six
   version: 1.17.0
   sha256: 4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274
   requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*'
+- pypi: https://files.pythonhosted.org/packages/bd/98/ef2b6fe2903e377cbe870c3b2800d62552f1e3dbe81ce49e1923c53d1c5c/h5py-3.16.0-cp313-cp313-manylinux_2_28_x86_64.whl
+  name: h5py
+  version: 3.16.0
+  sha256: 9300ad32dea9dfc5171f94d5f6948e159ed93e4701280b0f508773b3f582f402
+  requires_dist:
+  - numpy>=1.21.2
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/c5/9b/6ce1ead737fe496611bda600c263b9a11ae7bd8f41bb13f9bd7e0a2c37a4/platformdirs-4.12.3-py3-none-any.whl
   name: platformdirs
   version: 4.12.3
@@ -2150,6 +2760,15 @@ packages:
   version: 3.0.4
   sha256: 2d1b7d9308288661f56672b1b157d75fc536714d3638487bbea17b6318a78248
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/cc/e3/357baa8d2a9164a98dfd7ef9d3a58125df0ed981be909945bdd337be7194/msgspec-0.22.0-cp313-cp313-macosx_11_0_arm64.whl
+  name: msgspec
+  version: 0.22.0
+  sha256: 5aa24eb475d070ecbbe5b21080fc3ce4b0b76c60de25cfe0c9678d8fb44bb42f
+  requires_dist:
+  - tomli ; python_full_version < '3.11' and extra == 'toml'
+  - tomli-w ; extra == 'toml'
+  - pyyaml ; extra == 'yaml'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl
   name: pyyaml
   version: 6.0.3
@@ -2296,6 +2915,21 @@ packages:
   requires_dist:
   - six>=1.5
   requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*'
+- pypi: https://files.pythonhosted.org/packages/ec/d0/13860e6c8a06bb3fccc87e38966ed8ae98535aa44613b5fba0329ead0327/mudata-0.4.1-py3-none-any.whl
+  name: mudata
+  version: 0.4.1
+  sha256: adcaeecec06aec0d7576f5f18ea474b530d661759d775dbed67fcb91fb3c4c41
+  requires_dist:
+  - anndata>=0.11
+  - h5py
+  - numpy
+  - pandas>=1.4
+  - scipy
+  - scverse-misc[settings]>=0.1.1
+  - session-info2
+  - fsspec ; extra == 'io'
+  - zarr ; extra == 'io'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/ed/2a/f9da79cc98b37846e9e34bb8b093e7810b73d07561a736c9dbc2fb03927c/gtfreader-0.3.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_28_x86_64.whl
   name: gtfreader
   version: 0.3.0
@@ -2354,6 +2988,13 @@ packages:
   - pyranges1[docs] ; extra == 'all'
   - pyranges1[cli] ; extra == 'all'
   requires_python: '>=3.12.0'
+- pypi: https://files.pythonhosted.org/packages/f7/b9/c720e56858d4e1539503297ed37063e0c08e0f3541c41b777f6a800f75fa/pydantic_core-2.50.1-cp313-cp313-macosx_11_0_arm64.whl
+  name: pydantic-core
+  version: 2.50.1
+  sha256: 0c003c3b7f49debb893d2d85ae099ac5959c9839e2f330fadb1fcdf7a6594482
+  requires_dist:
+  - typing-extensions>=4.16.0
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl
   name: ghp-import
   version: 2.1.0
@@ -2371,6 +3012,11 @@ packages:
   requires_dist:
   - ukkonen ; extra == 'license'
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/fd/b6/e76e80fed5f2558273c7839e622f98095c9b36c719c7147e38e3c055cb70/google_crc32c-1.9.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
+  name: google-crc32c
+  version: 1.9.0
+  sha256: 0568b17ed90ac596f29400d99e243fd0cc6276766183def888d1bf8d1dc13827
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/fe/c4/225c87bae08c8b9ec99030cd48ae9c4eca050a59bf5c2255853e18c87b50/watchdog-6.0.0-cp313-cp313-macosx_11_0_arm64.whl
   name: watchdog
   version: 6.0.0
diff --git a/pyproject.toml b/pyproject.toml
index f185259..d01b48a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,6 +41,10 @@ dependencies = [
     "pyranges1>=1.4",
     # pandas writes and reads the annotation store as parquet.
     "pyarrow>=16",
+    # The assembled object, and the matrices it is assembled from. Imported
+    # inside functions, like pysam, so `cau --help` loads neither.
+    "anndata>=0.10",
+    "mudata>=0.3",
 ]
 
 [project.scripts]
diff --git a/src/crispr_analysis_utils/cli/__init__.py b/src/crispr_analysis_utils/cli/__init__.py
index 7b4895b..d3dc4fc 100644
--- a/src/crispr_analysis_utils/cli/__init__.py
+++ b/src/crispr_analysis_utils/cli/__init__.py
@@ -38,6 +38,10 @@
         "igvf_export",
         "Write the guide design in the IGVF per-guide metadata format.",
     ),
+    "mudata-builder": (
+        "mudata_builder",
+        "Assemble a gene and a guide count matrix into one MuData.",
+    ),
     "install-skills": (
         "install_skills",
         "Install the bundled Claude Code skills and agents.",
diff --git a/src/crispr_analysis_utils/cli/mudata_builder.py b/src/crispr_analysis_utils/cli/mudata_builder.py
new file mode 100644
index 0000000..6e105da
--- /dev/null
+++ b/src/crispr_analysis_utils/cli/mudata_builder.py
@@ -0,0 +1,101 @@
+"""``cau mudata-builder``: assemble one channel's matrices into a MuData."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from . import CommandError
+
+
+def add_arguments(parser: argparse.ArgumentParser) -> None:
+    """Declare the ``run`` action and its arguments."""
+    actions = parser.add_subparsers(dest="action", metavar="ACTION", required=True)
+    run_parser = actions.add_parser(
+        "run",
+        help="Assemble a gene and a guide count matrix into one MuData.",
+        description=(
+            "Assembly only: nothing is computed from the counts, no cell is "
+            "dropped and no guide is called. Writes mudata/mudata.h5mu, "
+            "run_metadata/ and logs/."
+        ),
+    )
+    run_parser.add_argument("--rna", required=True, help="The gene counts, as h5ad.")
+    run_parser.add_argument("--guide", required=True, help="The guide counts, as h5ad.")
+    run_parser.add_argument(
+        "--guide-metadata",
+        required=True,
+        help="guide_metadata.tsv from `cau igvf-export run`.",
+    )
+    run_parser.add_argument(
+        "--store",
+        required=True,
+        help=(
+            "The annotation store, which must be the one the guide metadata "
+            "was built against. Every gene type in it is used, so a lncRNA "
+            "feature is named like any other."
+        ),
+    )
+    run_parser.add_argument(
+        "--measurement-set",
+        required=True,
+        help=(
+            "The channel these matrices are. The cell index becomes "
+            "{barcode}_{measurement_set}, so channels can be concatenated."
+        ),
+    )
+    run_parser.add_argument("--outdir", required=True, help="Where the object goes.")
+    run_parser.add_argument(
+        "--genome", help="The reference FASTA, for its checksum. Nothing is read."
+    )
+    run_parser.add_argument(
+        "--gtf-accession",
+        help=(
+            "The GTF's IGVF portal id. Without it the object records the "
+            "file's name instead."
+        ),
+    )
+    run_parser.add_argument("--genome-accession", help="The genome's IGVF portal id.")
+    run_parser.add_argument(
+        "--covariates",
+        help=(
+            "Per-cell covariates, barcode in the first column. Carried into "
+            "the root obs unchanged."
+        ),
+    )
+
+
+def run(args: argparse.Namespace) -> int:
+    """Run the action."""
+    from ..mudata_builder import BuildError, run
+
+    try:
+        result = run(
+            args.rna,
+            args.guide,
+            args.guide_metadata,
+            args.store,
+            args.outdir,
+            measurement_set=args.measurement_set,
+            genome=args.genome,
+            gtf_accession=args.gtf_accession,
+            genome_accession=args.genome_accession,
+            covariates=args.covariates,
+        )
+    except (BuildError, FileNotFoundError, OSError) as error:
+        raise CommandError(str(error)) from error
+
+    found = result.metadata["counts"]
+    print(
+        f"{found['cells']} cells ({found['cells_both']} in both, "
+        f"{found['cells_gene_only']} gene only, "
+        f"{found['cells_guide_only']} guide only): "
+        f"{found['genes']} genes, {found['guides']} guides"
+    )
+    if found["genes_unannotated"] or found["guides_unannotated"]:
+        print(
+            f"Unannotated: {found['genes_unannotated']} genes, "
+            f"{found['guides_unannotated']} guides"
+        )
+    print(f"Outputs in {Path(result.outdir)}")
+    return 0
diff --git a/src/crispr_analysis_utils/guide_annotation/__init__.py b/src/crispr_analysis_utils/guide_annotation/__init__.py
index 4c887fc..64aafce 100644
--- a/src/crispr_analysis_utils/guide_annotation/__init__.py
+++ b/src/crispr_analysis_utils/guide_annotation/__init__.py
@@ -1,7 +1,7 @@
 """Annotate aligned guides against genes.
 
 `guide_alignment` answers where in the genome a guide is. This answers where
-in a gene: exon, intron or intergenic, whether it falls in a promoter, which
+in a gene: genic or intergenic, exon or intron, every promoter it sits in, which
 gene and transcript, and how far it is from the nearest protein-coding TSS.
 """
 
diff --git a/src/crispr_analysis_utils/guide_annotation/annotate.py b/src/crispr_analysis_utils/guide_annotation/annotate.py
index a1af97e..1648e28 100644
--- a/src/crispr_analysis_utils/guide_annotation/annotate.py
+++ b/src/crispr_analysis_utils/guide_annotation/annotate.py
@@ -1,4 +1,4 @@
-"""Place each aligned site in a gene: exon, intron, promoter or intergenic.
+"""Place each aligned site in a gene: genic or intergenic, exon or intron.
 
 Overlaps are decided on the protospacer, so a guide reaching into an exon
 counts as exonic. Distances and the position along a CDS are measured from
diff --git a/src/crispr_analysis_utils/mudata_builder.py b/src/crispr_analysis_utils/mudata_builder.py
new file mode 100644
index 0000000..01063cf
--- /dev/null
+++ b/src/crispr_analysis_utils/mudata_builder.py
@@ -0,0 +1,517 @@
+"""Assemble a MuData from a gene and a guide count matrix.
+
+Assembly only. Nothing is derived from the counts, no cell is dropped, no
+guide is called and no pair is proposed: the result says what was measured
+and what each feature is, and leaves every judgement to the steps after it.
+
+The layout follows the IGVF CRISPR pipeline's own object, so the two can be
+compared field by field: modalities ``gene`` and ``guide``, a cell index of
+``{barcode}_{measurement_set}``, raw counts in ``X``, and the annotation of
+each feature in ``var``.
+
+Cells are the union of the two matrices, not their intersection. A barcode
+seen in one modality and not the other is kept, and MuData's own ``obsm``
+masks record which modality holds it. Filtering is a later, visible decision.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from ._files import file_md5, write_json
+from ._version import __version__
+from .guide_annotation.db import _unversioned
+
+GENE_COLUMNS = ("symbol", "gene_type", "gene_chr", "gene_start", "gene_end")
+"""What `var` holds for a gene, beside the id that indexes it."""
+
+MODALITIES = ("gene", "guide")
+"""The two modalities, in the order the object carries them."""
+
+TABLE = "mudata.h5mu"
+"""The one output."""
+
+RESULTS_DIR = "mudata"
+METADATA_DIR = "run_metadata"
+STEP = "mudata_builder"
+LOG_FILE = "mudata_builder.log"
+_LOG_FORMAT = "%(asctime)s %(levelname)s %(name)s: %(message)s"
+
+logger = logging.getLogger(__name__)
+
+
+def _now() -> str:
+    """The moment, as the run record writes it."""
+    return datetime.now(UTC).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+
+def _stamp() -> str:
+    """The run record's file stamp."""
+    return datetime.now(UTC).strftime("%Y%m%dT%H%M%SZ")
+
+
+class BuildError(RuntimeError):
+    """The inputs cannot be assembled into one object."""
+
+
+def read_counts(path: str | os.PathLike[str]):
+    """Read one count matrix.
+
+    Parameters
+    ----------
+    path
+        An ``.h5ad`` written by kb, or anything anndata can read.
+
+    Returns
+    -------
+    anndata.AnnData
+        The matrix as it was stored, layers and all.
+
+    Raises
+    ------
+    BuildError
+        The file is missing or unreadable.
+    """
+    import anndata as ad
+
+    path = Path(path)
+    if not path.is_file():
+        raise BuildError(f"The count matrix {path} does not exist.")
+    try:
+        return ad.read_h5ad(path)
+    except (OSError, KeyError, ValueError) as error:
+        raise BuildError(f"{path} is not a readable h5ad: {error}") from error
+
+
+def gene_var(store, ids: pd.Index) -> pd.DataFrame:
+    """The annotation of every gene in the matrix, in the matrix's own order.
+
+    Parameters
+    ----------
+    store
+        A loaded annotation `Store`. Every gene type it holds is used, not
+        only the ones guides are annotated against.
+    ids
+        The matrix's gene ids, versioned or not.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Indexed by the ids as given, so it can be attached to ``var``
+        directly. A gene the store does not hold keeps its row with the
+        annotation empty, rather than being dropped.
+    """
+    # Every gene the store holds, not only the annotated types: the counts
+    # come from a transcriptome built on the whole GTF, so a lncRNA feature
+    # is a real row of the matrix and deserves its name.
+    genes = store.rows("gene", primary=False).drop_duplicates("ensg").set_index("ensg")
+    annotation = pd.DataFrame(
+        {
+            "symbol": genes["gene_symbol"],
+            "gene_type": genes["gene_type"].astype("string"),
+            "gene_chr": genes["Chromosome"].astype("string"),
+            "gene_start": genes["Start"],
+            "gene_end": genes["End"],
+        }
+    )
+    # kb indexes genes as the GTF names them, with the version; the store
+    # strips it. The rule has to be the store's own, or a PAR_Y gene, whose
+    # id ends in neither, comes back annotated with nothing.
+    out = annotation.reindex(_unversioned(pd.Series(ids, dtype="string")))
+    out.index = pd.Index(ids, name="gene_id")
+    return out
+
+
+def guide_var(path: str | os.PathLike[str], ids: pd.Index) -> pd.DataFrame:
+    """The guide design, in the matrix's own order.
+
+    Parameters
+    ----------
+    path
+        ``guide_metadata.tsv`` from `cau igvf-export`, one row per guide.
+    ids
+        The matrix's guide ids.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Every column of the file except the id that indexes it. A guide the
+        file does not describe keeps its row with the annotation empty.
+
+    Raises
+    ------
+    BuildError
+        The file is missing, or has no ``guide_id`` column.
+    """
+    path = Path(path)
+    if not path.is_file():
+        raise BuildError(f"The guide metadata {path} does not exist.")
+    frame = pd.read_csv(path, sep="\t", dtype=str)
+    if "guide_id" not in frame.columns:
+        raise BuildError(
+            f"{path} has no guide_id column, so it is not a guide metadata"
+            " table. `cau igvf-export run` writes one."
+        )
+    frame = frame.drop_duplicates("guide_id").set_index("guide_id")
+    out = frame.reindex(pd.Index(ids, dtype="string"))
+    out.index = pd.Index(ids, name="guide_id")
+    return out
+
+
+def reference_block(
+    store,
+    *,
+    gtf_accession: str | None = None,
+    genome: str | os.PathLike[str] | None = None,
+    genome_accession: str | None = None,
+) -> dict:
+    """What the object says about the annotation it was built against.
+
+    Carried in ``uns['reference']`` so the assembled file answers "which
+    GENCODE, which genome" without the run record beside it.
+
+    Parameters
+    ----------
+    store
+        A loaded `Store`; its provenance names the GTF it was built from.
+    gtf_accession
+        The GTF's IGVF portal id, which no local file carries. The CRISPR
+        pipeline's inputs have it; without it the field falls back to the
+        file's name, so it is never empty and never a guess.
+    genome
+        The reference FASTA, for its checksum. Nothing is read from it.
+    genome_accession
+        The genome's IGVF portal id, falling back to the file's name.
+
+    Returns
+    -------
+    dict
+        ``gtf``, ``gtf_md5``, ``gtf_accession``, ``genome``, ``genome_md5``,
+        each file named rather than pathed, since the object is shared and a
+        path is not,
+        ``genome_accession``, ``gene_types`` and ``cau_version``. A checksum
+        is empty when the file is no longer where the store recorded it: a
+        path is a claim, a checksum is evidence. An accession falls back to
+        the file's name, so the field always says which file was meant.
+    """
+    gtf = str(store.provenance.get("gtf", ""))
+    # The name, never the path: this travels with the object, and a path
+    # carries the filesystem it was built on.
+    block = {
+        "gtf": Path(gtf).name if gtf else "",
+        "gtf_md5": file_md5(gtf) if gtf and Path(gtf).is_file() else "",
+        "gtf_accession": gtf_accession or (Path(gtf).name if gtf else ""),
+        "genome": Path(genome).name if genome else "",
+        "genome_md5": (file_md5(genome) if genome and Path(genome).is_file() else ""),
+        "genome_accession": (genome_accession or (Path(genome).name if genome else "")),
+        "gene_types": list(store.provenance.get("gene_types", [])),
+        "cau_version": str(store.provenance.get("cau_version", "")),
+    }
+    if genome and not Path(genome).is_file():
+        raise BuildError(f"The genome {genome} does not exist.")
+    return block
+
+
+def check_same_store(
+    guide_metadata: str | os.PathLike[str], store_path: str | os.PathLike[str]
+) -> str:
+    """Refuse a store other than the one the guide annotation was built on.
+
+    The guide table carries no provenance of its own, but the run record
+    beside it does. Annotating guides against one GENCODE and genes against
+    another produces an object whose two halves disagree, and nothing
+    downstream would notice.
+
+    Parameters
+    ----------
+    guide_metadata
+        ``guide_metadata.tsv``; its ``../run_metadata`` is where the record
+        of the run that wrote it lives.
+    store_path
+        The store this build was given.
+
+    Returns
+    -------
+    str
+        What was compared, for the log. Empty when no record was found, which
+        is not an error: the table may have been copied away from its run.
+
+    Raises
+    ------
+    BuildError
+        The record names a different store.
+    """
+    records = Path(guide_metadata).resolve().parent.parent / "run_metadata"
+    if not records.is_dir():
+        return ""
+    written = sorted(records.glob("igvf_export_*.json"))
+    if not written:
+        return ""
+    state = json.loads(written[-1].read_text(encoding="utf-8"))
+    used = state.get("inputs", {}).get("store", "")
+    if not used:
+        return ""
+    if Path(used).resolve() != Path(store_path).resolve():
+        raise BuildError(
+            f"The guide metadata was written against {used}, but this build "
+            f"was given {store_path}. Genes and guides would be annotated "
+            "against different GENCODE releases. Pass the same store, or "
+            "re-run `cau igvf-export run` against this one."
+        )
+    return used
+
+
+def build(
+    rna,
+    guide,
+    *,
+    gene_table: pd.DataFrame,
+    guide_table: pd.DataFrame,
+    measurement_set: str,
+    reference: dict | None = None,
+    covariates: pd.DataFrame | None = None,
+):
+    """Assemble the two matrices into one MuData.
+
+    Parameters
+    ----------
+    rna, guide
+        The count matrices, as `read_counts` returns them.
+    gene_table, guide_table
+        What `gene_var` and `guide_var` return, already in each matrix's
+        order.
+    measurement_set
+        The channel this pair of matrices is, which the cell index carries so
+        two channels can be concatenated without their barcodes colliding.
+    reference
+        What `reference_block` returns, stored in ``uns['reference']``.
+    covariates
+        One row per cell, indexed by barcode. Its columns join the root
+        ``obs``. Nothing is computed; whatever is given is carried.
+
+    Returns
+    -------
+    mudata.MuData
+        Modalities ``gene`` and ``guide``, cells the union of the two.
+
+    Raises
+    ------
+    BuildError
+        A matrix and its annotation disagree on shape.
+    """
+    import mudata as md
+
+    for name, matrix, table in (
+        ("gene", rna, gene_table),
+        ("guide", guide, guide_table),
+    ):
+        if len(table) != matrix.n_vars:
+            raise BuildError(
+                f"The {name} annotation has {len(table)} rows for "
+                f"{matrix.n_vars} features. They must line up."
+            )
+
+    rna = rna.copy()
+    guide = guide.copy()
+    rna.var = gene_table
+    guide.var = guide_table
+    for matrix in (rna, guide):
+        # {barcode}_{measurement_set}, as the pipeline indexes cells, so a
+        # concatenation across channels cannot collide.
+        barcodes = pd.Index(matrix.obs_names, dtype="string")
+        matrix.obs["barcode"] = barcodes
+        matrix.obs["measurement_set"] = measurement_set
+        matrix.obs_names = pd.Index(barcodes + "_" + measurement_set)
+        # kb names the index "barcode", and anndata refuses an index whose
+        # name is also a column holding different values.
+        matrix.obs.index.name = None
+        matrix.var.index.name = None
+
+    # MuData takes the union of the two indexes and records membership in
+    # obsm, so a barcode seen in one modality only is kept and visible.
+    data = md.MuData({"gene": rna, "guide": guide})
+    data.uns["reference"] = dict(reference or {})
+    data.uns["measurement_set"] = measurement_set
+
+    if covariates is not None and not covariates.empty:
+        joined = covariates.reindex(
+            pd.Index(data.obs_names, dtype="string").str.rsplit("_", n=1).str[0]
+        )
+        joined.index = data.obs_names
+        for column in joined.columns:
+            data.obs[column] = joined[column]
+    return data
+
+
+@dataclass
+class RunResult:
+    """What `run` produced.
+
+    Attributes
+    ----------
+    outdir
+        The output folder.
+    data
+        The assembled MuData.
+    metadata
+        The record written under ``run_metadata/``.
+    """
+
+    outdir: Path
+    data: object
+    metadata: dict = field(default_factory=dict)
+
+
+def run(
+    rna: str | os.PathLike[str],
+    guide: str | os.PathLike[str],
+    guide_metadata: str | os.PathLike[str],
+    store: str | os.PathLike[str],
+    outdir: str | os.PathLike[str],
+    *,
+    measurement_set: str,
+    genome: str | os.PathLike[str] | None = None,
+    gtf_accession: str | None = None,
+    genome_accession: str | None = None,
+    covariates: str | os.PathLike[str] | None = None,
+) -> RunResult:
+    """Assemble one channel's matrices into a MuData, and write it.
+
+    Parameters
+    ----------
+    rna, guide
+        The two count matrices, as ``.h5ad``.
+    guide_metadata
+        ``guide_metadata.tsv`` from `cau igvf-export`.
+    store
+        The annotation store, which must be the one the guide metadata was
+        built against.
+    outdir
+        Where the object goes. Created if it does not exist; nothing is ever
+        written outside it.
+    measurement_set
+        The channel these matrices are.
+    genome, gtf_accession, genome_accession
+        Recorded in ``uns['reference']``; nothing is read from the genome
+        but its checksum.
+    covariates
+        A table of per-cell covariates, indexed by barcode in its first
+        column. Carried into the root ``obs`` unchanged.
+
+    Returns
+    -------
+    RunResult
+        The object, and the record of the run.
+    """
+    from .guide_annotation import load_store
+
+    outdir = Path(outdir)
+    outdir.mkdir(parents=True, exist_ok=True)
+    (outdir / "logs").mkdir(exist_ok=True)
+    (outdir / METADATA_DIR).mkdir(exist_ok=True)
+    (outdir / RESULTS_DIR).mkdir(exist_ok=True)
+    record = outdir / METADATA_DIR / f"{STEP}_{_stamp()}.json"
+
+    state: dict = {
+        "step": STEP,
+        "complete": False,
+        "error": None,
+        "started": _now(),
+        "finished": None,
+        "cau_version": __version__,
+        "inputs": {},
+        "parameters": {"measurement_set": measurement_set},
+        "counts": {},
+    }
+    handler = logging.FileHandler(outdir / "logs" / LOG_FILE, encoding="utf-8")
+    handler.setFormatter(logging.Formatter(_LOG_FORMAT))
+    package = logging.getLogger(__name__.rsplit(".", 1)[0])
+    package.addHandler(handler)
+    if package.level == logging.NOTSET:
+        package.setLevel(logging.INFO)
+    propagate = package.propagate
+    package.propagate = False
+    try:
+        compared = check_same_store(guide_metadata, store)
+        if compared:
+            logger.info("Guide metadata and genes share the store %s", compared)
+        else:
+            logger.warning(
+                "No run record beside the guide metadata, so the store it was "
+                "built against could not be checked."
+            )
+
+        loaded = load_store(store)
+        rna_counts = read_counts(rna)
+        guide_counts = read_counts(guide)
+        for name, path in (
+            ("rna", rna),
+            ("guide", guide),
+            ("guide_metadata", guide_metadata),
+            ("store", store),
+        ):
+            state["inputs"][name] = str(path)
+            state["inputs"][f"{name}_md5"] = file_md5(path)
+
+        covariate_table = None
+        if covariates is not None:
+            covariate_table = pd.read_csv(covariates, sep="\t", index_col=0)
+            state["inputs"]["covariates"] = str(covariates)
+            state["inputs"]["covariates_md5"] = file_md5(covariates)
+
+        reference = reference_block(
+            loaded,
+            gtf_accession=gtf_accession,
+            genome=genome,
+            genome_accession=genome_accession,
+        )
+        state["parameters"]["reference"] = reference
+
+        data = build(
+            rna_counts,
+            guide_counts,
+            gene_table=gene_var(loaded, rna_counts.var_names),
+            guide_table=guide_var(guide_metadata, guide_counts.var_names),
+            measurement_set=measurement_set,
+            reference=reference,
+            covariates=covariate_table,
+        )
+        data.write(outdir / RESULTS_DIR / TABLE)
+
+        # mudata may hand the membership mask back as a column vector, so
+        # flatten before counting rather than trusting its shape.
+        in_gene = np.asarray(data.obsm["gene"]).ravel()
+        in_guide = np.asarray(data.obsm["guide"]).ravel()
+        state["counts"] = {
+            "cells": int(data.n_obs),
+            "cells_gene_only": int((in_gene & ~in_guide).sum()),
+            "cells_guide_only": int((in_guide & ~in_gene).sum()),
+            "cells_both": int((in_gene & in_guide).sum()),
+            "genes": int(data["gene"].n_vars),
+            "guides": int(data["guide"].n_vars),
+            "genes_unannotated": int(data["gene"].var["symbol"].isna().sum()),
+            "guides_unannotated": int(data["guide"].var["spacer"].isna().sum()),
+        }
+        state["complete"] = True
+        state["finished"] = _now()
+        write_json(record, state)
+        logger.info("MuData complete: %s", state["counts"])
+        return RunResult(outdir=outdir, data=data, metadata=state)
+    except Exception as error:
+        state["error"] = f"{type(error).__name__}: {error}"
+        state["finished"] = _now()
+        write_json(record, state)
+        logger.error("MuData build failed: %s", state["error"])
+        raise
+    finally:
+        package.removeHandler(handler)
+        package.propagate = propagate
+        handler.close()
diff --git a/src/crispr_analysis_utils/skills/cau-mudata-builder/SKILL.md b/src/crispr_analysis_utils/skills/cau-mudata-builder/SKILL.md
new file mode 100644
index 0000000..e4739c5
--- /dev/null
+++ b/src/crispr_analysis_utils/skills/cau-mudata-builder/SKILL.md
@@ -0,0 +1,87 @@
+---
+name: cau-mudata-builder
+description: >-
+  Assemble a gene and a guide count matrix into one MuData with
+  crispr-analysis-utils (the `cau mudata-builder` command, or
+  `crispr_analysis_utils.mudata_builder`). Use when a user has kb or Cell
+  Ranger output for a channel and wants the single object the IGVF CRISPR
+  pipeline works in, when they ask how to attach gene and guide annotation
+  to their counts, or when they want an h5mu to hand to a QC or inference
+  step. Covers what goes in var, obs and uns, why cells are the union rather
+  than the intersection, and what the step deliberately does not do.
+---
+
+# Build a MuData (`cau mudata-builder`)
+
+```bash
+cau mudata-builder run \
+    --rna counts_unfiltered/adata.h5ad \
+    --guide counts_unfiltered_modified/adata.h5ad \
+    --guide-metadata results/igvf/guide_metadata.tsv \
+    --store gencode.v43.annotation.parquet \
+    --measurement-set IGVFDS8842PXCC \
+    --gtf-accession IGVFFI9561BASO \
+    --outdir results/
+```
+
+## Ask before running
+
+- **Which GENCODE?** It must be the same release the guides were annotated
+  against, or genes and guides describe different genomes. Ask rather than
+  reach for whatever store is nearest. The run refuses a store other than the
+  one the guide metadata's run record names, and warns when there is no
+  record to check against.
+- **Which channel?** `--measurement-set` goes into the cell index as
+  `{barcode}_{measurement_set}`, so two channels concatenate without their
+  barcodes colliding. There is no sensible default.
+- **The portal ids.** `--gtf-accession` and `--genome-accession` take IGVF
+  accessions; the CRISPR pipeline's inputs carry them. Without one the object
+  records the file's name instead, so the field is never empty and never a
+  guess.
+
+## What it writes
+
+`mudata/mudata.h5mu`, plus `run_metadata/` and `logs/`.
+
+| Where | What |
+| --- | --- |
+| `mod['gene'].X` | the gene counts, untouched, with the kb layers beside them |
+| `mod['gene'].var` | `symbol`, `gene_type`, `gene_chr`, `gene_start`, `gene_end` |
+| `mod['guide'].X` | the guide counts, untouched |
+| `mod['guide'].var` | every column of `guide_metadata.tsv` |
+| `obs` | `barcode` and `measurement_set`, plus any `--covariates` given |
+| `obsm['gene']`, `obsm['guide']` | which modality holds each cell |
+| `uns['reference']` | the GTF and genome: name, md5 and accession |
+
+## What it does not do
+
+**Nothing is computed from the counts.** No per-cell or per-feature
+statistics, no QC, no filtering, no doublet calls, no guide assignment, no
+pairs to test. Those are later steps, and each is a judgement this one has no
+business making.
+
+## Four things worth knowing
+
+- **Cells are the union, not the intersection.** A barcode seen in one
+  modality and not the other is kept, and `obsm` records which modality holds
+  it. On a real channel that is a million barcodes against 200,000 seen by
+  both, so an intersection would discard most of the object before anyone
+  looked at it. Filtering comes later, where it is visible.
+- **Every gene type is annotated**, not only protein-coding. The counts come
+  from a transcriptome built on the whole GTF, so two thirds of the features
+  are lncRNA, pseudogenes and small RNAs; annotating only the coding genes
+  would leave them nameless.
+- **PAR genes appear twice.** 47 features carry a `_PAR_Y` suffix and have a
+  chrX twin with identical sequence, so kallisto cannot tell their reads
+  apart. Both rows are carried through untouched. Anything downstream that
+  sums or de-duplicates them is making a choice; this step does not.
+- **The ids carry GENCODE versions.** kb indexes genes as `ENSG00000290825.1`
+  while the store drops the version, and the two are joined on the store's own
+  rule, which strips a trailing `.N` and leaves `_PAR_Y` alone. A different
+  rule silently gives the chrY copy the chrX gene's coordinates.
+
+## Then what
+
+A QC step filters this object; after that it is comparable with the IGVF
+pipeline's own. See `cau-workflow` for where this sits, and the guide design
+steps that produce `guide_metadata.tsv`.
diff --git a/src/crispr_analysis_utils/skills/cau-workflow/SKILL.md b/src/crispr_analysis_utils/skills/cau-workflow/SKILL.md
index 3204d63..3476349 100644
--- a/src/crispr_analysis_utils/skills/cau-workflow/SKILL.md
+++ b/src/crispr_analysis_utils/skills/cau-workflow/SKILL.md
@@ -25,6 +25,11 @@ detail; this one decides which step the user is actually asking for.
 | 3. Which element of the screen does it test? | `cau element-mapping run` | `cau-element-mapping` |
 | 4. Write the IGVF table | `cau igvf-export run` | `cau-igvf-export` |
 
+Once a screen has been sequenced, `cau-mudata-builder` assembles a channel's
+gene and guide counts into one MuData, taking `guide_metadata.tsv` from step 4
+as the guide annotation. It assembles only: no statistics, no filtering, no
+guide calls.
+
 Two modules sit outside the chain and need no predecessor:
 `cau-accession` (a lab-independent id encoded from the protospacer) and
 `cau-normalization` (counts per million, for a screen's count matrix).
diff --git a/tests/test_mudata_builder.py b/tests/test_mudata_builder.py
new file mode 100644
index 0000000..6db88d4
--- /dev/null
+++ b/tests/test_mudata_builder.py
@@ -0,0 +1,214 @@
+"""The MuData builder: what it assembles, and what it refuses."""
+
+from __future__ import annotations
+
+import json
+
+import numpy as np
+import pandas as pd
+import pytest
+from guide_annotation_fixtures import write_gtf
+
+from crispr_analysis_utils.guide_annotation import build_store
+from crispr_analysis_utils.mudata_builder import (
+    BuildError,
+    build,
+    check_same_store,
+    gene_var,
+    guide_var,
+    read_counts,
+    reference_block,
+    run,
+)
+
+MEASUREMENT_SET = "IGVFDS0000TEST"
+
+
+@pytest.fixture
+def store(tmp_path):
+    return build_store(write_gtf(tmp_path / "test.gtf"), tmp_path / "store.parquet")
+
+
+def counts(path, barcodes, features, index_name):
+    """A minimal h5ad, shaped like the ones kb writes."""
+    import anndata as ad
+
+    frame = pd.DataFrame(index=pd.Index(features, name=index_name))
+    obs = pd.DataFrame(index=pd.Index(barcodes, name="barcode"))
+    matrix = ad.AnnData(
+        X=np.arange(len(barcodes) * len(features), dtype="float32").reshape(
+            len(barcodes), len(features)
+        ),
+        obs=obs,
+        var=frame,
+    )
+    matrix.write_h5ad(path)
+    return path
+
+
+def guide_table(path, guide_ids):
+    pd.DataFrame(
+        {
+            "guide_id": guide_ids,
+            "spacer": ["A" * 20] * len(guide_ids),
+            "type": ["targeting", "safe-targeting", "non-targeting"][: len(guide_ids)],
+        }
+    ).to_csv(path, sep="\t", index=False)
+    return path
+
+
+def test_every_gene_type_is_annotated_not_only_the_coding_ones(store):
+    # DDD is lncRNA. The counts come from the whole GTF, so it is a real row.
+    ids = pd.Index(["ENSG00000000001.5", "ENSG00000000004.2"])
+    out = gene_var(store, ids)
+    assert list(out.index) == list(ids)
+    assert out["symbol"].tolist() == ["AAA", "DDD"]
+    assert out["gene_type"].tolist() == ["protein_coding", "lncRNA"]
+
+
+def test_a_gene_the_store_does_not_hold_keeps_its_row(store):
+    out = gene_var(store, pd.Index(["ENSG00000000001.5", "ENSG99999999999.1"]))
+    assert len(out) == 2
+    assert pd.isna(out["symbol"].iloc[1])
+
+
+def test_the_version_rule_is_the_stores_so_par_y_keeps_its_suffix(store):
+    # Splitting on the first dot would give this the chrX gene's annotation.
+    out = gene_var(store, pd.Index(["ENSG00000000001.5_PAR_Y"]))
+    assert pd.isna(out["symbol"].iloc[0])
+
+
+def test_guide_var_keeps_every_column_and_the_matrix_order(tmp_path):
+    path = guide_table(tmp_path / "guides.tsv", ["g2", "g1", "g3"])
+    out = guide_var(path, pd.Index(["g1", "g2"]))
+    assert list(out.index) == ["g1", "g2"]
+    assert "spacer" in out.columns and "type" in out.columns
+    assert "guide_id" not in out.columns
+
+
+def test_the_four_guide_classes_survive_untouched(tmp_path):
+    path = guide_table(tmp_path / "guides.tsv", ["g1", "g2", "g3"])
+    out = guide_var(path, pd.Index(["g1", "g2", "g3"]))
+    assert out["type"].tolist() == ["targeting", "safe-targeting", "non-targeting"]
+
+
+def test_a_table_without_guide_id_is_refused(tmp_path):
+    path = tmp_path / "bad.tsv"
+    pd.DataFrame({"name": ["g1"]}).to_csv(path, sep="\t", index=False)
+    with pytest.raises(BuildError, match="no guide_id column"):
+        guide_var(path, pd.Index(["g1"]))
+
+
+def test_cells_are_the_union_and_obsm_says_which_modality_holds_each(store, tmp_path):
+    rna = read_counts(
+        counts(tmp_path / "r.h5ad", ["c1", "c2"], ["ENSG00000000001.5"], "gene_id")
+    )
+    guide = read_counts(counts(tmp_path / "g.h5ad", ["c2", "c3"], ["g1"], "guide_id"))
+    data = build(
+        rna,
+        guide,
+        gene_table=gene_var(store, rna.var_names),
+        guide_table=guide_var(
+            guide_table(tmp_path / "gt.tsv", ["g1"]), guide.var_names
+        ),
+        measurement_set=MEASUREMENT_SET,
+    )
+    assert data.n_obs == 3
+    assert list(data.obs_names) == [f"c{i}_{MEASUREMENT_SET}" for i in (1, 2, 3)]
+    assert np.asarray(data.obsm["gene"]).ravel().tolist() == [True, True, False]
+    assert np.asarray(data.obsm["guide"]).ravel().tolist() == [False, True, True]
+
+
+def test_the_cell_index_carries_the_measurement_set(store, tmp_path):
+    rna = read_counts(
+        counts(tmp_path / "r.h5ad", ["c1"], ["ENSG00000000001.5"], "gene_id")
+    )
+    guide = read_counts(counts(tmp_path / "g.h5ad", ["c1"], ["g1"], "guide_id"))
+    data = build(
+        rna,
+        guide,
+        gene_table=gene_var(store, rna.var_names),
+        guide_table=guide_var(
+            guide_table(tmp_path / "gt.tsv", ["g1"]), guide.var_names
+        ),
+        measurement_set=MEASUREMENT_SET,
+    )
+    assert data["gene"].obs["barcode"].tolist() == ["c1"]
+    assert data["gene"].obs["measurement_set"].tolist() == [MEASUREMENT_SET]
+
+
+def test_an_annotation_of_the_wrong_length_is_refused(store, tmp_path):
+    rna = read_counts(
+        counts(tmp_path / "r.h5ad", ["c1"], ["ENSG00000000001.5"], "gene_id")
+    )
+    guide = read_counts(counts(tmp_path / "g.h5ad", ["c1"], ["g1"], "guide_id"))
+    with pytest.raises(BuildError, match="must line up"):
+        build(
+            rna,
+            guide,
+            gene_table=pd.DataFrame(index=["a", "b"]),
+            guide_table=guide_var(
+                guide_table(tmp_path / "gt.tsv", ["g1"]), guide.var_names
+            ),
+            measurement_set=MEASUREMENT_SET,
+        )
+
+
+def test_the_reference_block_names_files_rather_than_pathing_them(store, tmp_path):
+    block = reference_block(store, gtf_accession="IGVFFI9561BASO")
+    assert "/" not in block["gtf"]
+    assert block["gtf_accession"] == "IGVFFI9561BASO"
+    assert len(block["gtf_md5"]) == 32
+
+
+def test_an_accession_falls_back_to_the_file_name(store):
+    block = reference_block(store)
+    assert block["gtf_accession"] == block["gtf"] != ""
+
+
+def test_a_genome_that_does_not_exist_is_refused(store, tmp_path):
+    with pytest.raises(BuildError, match="does not exist"):
+        reference_block(store, genome=tmp_path / "absent.fa")
+
+
+def test_a_different_store_than_the_guides_were_built_on_is_refused(tmp_path):
+    results = tmp_path / "results"
+    (results / "igvf").mkdir(parents=True)
+    (results / "run_metadata").mkdir()
+    guides = results / "igvf" / "guide_metadata.tsv"
+    guide_table(guides, ["g1"])
+    (results / "run_metadata" / "igvf_export_20260101T000000Z.json").write_text(
+        json.dumps({"inputs": {"store": str(tmp_path / "other.parquet")}}),
+        encoding="utf-8",
+    )
+    with pytest.raises(BuildError, match="different GENCODE"):
+        check_same_store(guides, tmp_path / "mine.parquet")
+
+
+def test_no_run_record_is_not_an_error(tmp_path):
+    guides = guide_table(tmp_path / "guide_metadata.tsv", ["g1"])
+    assert check_same_store(guides, tmp_path / "mine.parquet") == ""
+
+
+def test_a_run_writes_the_object_and_its_record(store, tmp_path):
+    rna = counts(tmp_path / "r.h5ad", ["c1", "c2"], ["ENSG00000000001.5"], "gene_id")
+    guide = counts(tmp_path / "g.h5ad", ["c2"], ["g1"], "guide_id")
+    result = run(
+        rna,
+        guide,
+        guide_table(tmp_path / "gt.tsv", ["g1"]),
+        tmp_path / "store.parquet",
+        tmp_path / "out",
+        measurement_set=MEASUREMENT_SET,
+    )
+    assert (tmp_path / "out" / "mudata" / "mudata.h5mu").is_file()
+    assert result.metadata["complete"] is True
+    assert result.metadata["counts"]["cells"] == 2
+    assert result.metadata["counts"]["cells_gene_only"] == 1
+    assert result.metadata["counts"]["cells_both"] == 1
+    assert result.metadata["parameters"]["reference"]["gtf"] != ""
+
+
+def test_a_missing_matrix_is_refused(store, tmp_path):
+    with pytest.raises(BuildError, match="does not exist"):
+        read_counts(tmp_path / "absent.h5ad")
diff --git a/tests/test_skills.py b/tests/test_skills.py
index 633cec0..cb76cfd 100644
--- a/tests/test_skills.py
+++ b/tests/test_skills.py
@@ -32,6 +32,7 @@
     "cau-guide-annotation": ("guide_annotation",),
     "cau-element-mapping": ("element_mapping",),
     "cau-igvf-export": ("igvf_export",),
+    "cau-mudata-builder": ("mudata_builder",),
 }
 
 # Modules that are plumbing rather than analysis, so need no skill.
diff --git a/uv.lock b/uv.lock
index 4c6ee0a..aed96ba 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,6 +10,46 @@ resolution-markers = [
     "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
+[[package]]
+name = "anndata"
+version = "0.13.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "array-api-compat" },
+    { name = "h5py" },
+    { name = "legacy-api-wrap" },
+    { name = "natsort" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "scipy" },
+    { name = "scverse-misc", extra = ["settings"] },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
+    { name = "zarr" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/27/94/ded0e45490ad65d1232d3bc3bc618fb24d33d8e87653baca1553074261be/anndata-0.13.4.tar.gz", hash = "sha256:fadee3955b4b826dd23d9c0aa296147d8624f8222210b6c8abc3c115a111dce4", size = 2376085, upload-time = "2026-09-18T12:58:50.131Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/54/23/f1b1af76bc5a1845d5fcad297e4c460223a7670f1ca15e167d7dcf32aaab/anndata-0.13.4-py3-none-any.whl", hash = "sha256:028c4e4f968a68965d3d0fc465c947890fa8e5aa6d2506104590f46080c7736d", size = 190231, upload-time = "2026-09-18T12:58:48.639Z" },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
+]
+
+[[package]]
+name = "array-api-compat"
+version = "1.15.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/86/12/65d297d3e425cb7fe5247b7ec14b4ed83539e9b5e89b65c15ea7ee274182/array_api_compat-1.15.0.tar.gz", hash = "sha256:53c5f922491bf15f62847afafc4e39eedfae57d218988fefb8cce39c2a9b3dea", size = 129305, upload-time = "2026-06-07T20:53:24.964Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/86/16/1a8fd2b19544b84575cf84ef7aa3ad4c173b756d5f087c91f85d1b295777/array_api_compat-1.15.0-py3-none-any.whl", hash = "sha256:7b1b9c53269061403fd5f45a8de349f16e7887653328bfa0c5f2d45299ff0a8e", size = 79113, upload-time = "2026-06-07T20:53:23.621Z" },
+]
+
 [[package]]
 name = "babel"
 version = "2.18.0"
@@ -204,6 +244,8 @@ name = "crispr-analysis-utils"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
+    { name = "anndata" },
+    { name = "mudata" },
     { name = "numpy" },
     { name = "pandas" },
     { name = "pyarrow" },
@@ -224,6 +266,8 @@ docs = [
 
 [package.metadata]
 requires-dist = [
+    { name = "anndata", specifier = ">=0.10" },
+    { name = "mudata", specifier = ">=0.3" },
     { name = "numpy", specifier = ">=1.23" },
     { name = "pandas", specifier = ">=1.5" },
     { name = "pyarrow", specifier = ">=16" },
@@ -251,6 +295,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/02/08/9c41fb51ab5b43eb21674aff13df270e8ba6c4b29c8624e328dc7a9482af/distlib-0.4.3-py2.py3-none-any.whl", hash = "sha256:4b0ce306c966eb73bc3a7b6abad017c556dadd92c44701562cd528ac7fde4d5b", size = 470628, upload-time = "2026-06-12T08:04:50.506Z" },
 ]
 
+[[package]]
+name = "donfig"
+version = "0.8.1.post1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/25/71/80cc718ff6d7abfbabacb1f57aaa42e9c1552bfdd01e64ddd704e4a03638/donfig-0.8.1.post1.tar.gz", hash = "sha256:3bef3413a4c1c601b585e8d297256d0c1470ea012afa6e8461dc28bfb7c23f52", size = 19506, upload-time = "2024-05-23T14:14:31.513Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0c/d5/c5db1ea3394c6e1732fb3286b3bd878b59507a8f77d32a2cebda7d7b7cd4/donfig-0.8.1.post1-py3-none-any.whl", hash = "sha256:2a3175ce74a06109ff9307d90a230f81215cbac9a751f4d1c6194644b8204f9d", size = 21592, upload-time = "2024-05-23T14:13:55.283Z" },
+]
+
 [[package]]
 name = "filelock"
 version = "4.0.12"
@@ -272,6 +328,34 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619", size = 11034, upload-time = "2022-05-02T15:47:14.552Z" },
 ]
 
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+name = "google-crc32c"
+version = "1.9.0"
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