refactor(cytoland): unify VSUNet rotation TTA on rotation_tta_transforms

.claude/package.json

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+{"type":"commonjs"}

.claude/skills/airtable-build-collection/SKILL.md

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+---
+name: airtable-build-collection
+description: Query Airtable MCP to fetch experiment metadata and generate a DynaCLR collection YAML for training
+---
+
+# Build Collection from Airtable
+
+Build a collection YAML for DynaCLR training by querying the Computational Imaging Database on Airtable.
+
+## Airtable Configuration
+
+- **Base ID**: `app8vqaoWyOwa0sB5` (Computational Imaging Database)
+- **Table ID**: `tblaFzrDMlVZHPZIj` (Datasets)
+
+Key fields in the Datasets table:
+
+| Field | Description |
+|---|---|
+| `dataset` | Experiment name (e.g. `2025_07_24_A549_SEC61_TOMM20_G3BP1_ZIKV`) |
+| `well_id` | Well path (e.g. `B/2`) |
+| `fov` | FOV identifier |
+| `cell_state` | Condition label (e.g. `infected`, `uninfected`) |
+| `marker` | Protein marker (e.g. `SEC61B`, `TOMM20`, `pAL10`) |
+| `organelle` | Target organelle |
+| `perturbation` | Perturbation applied |
+| `hours_post_perturbation` | HPI at imaging start |
+| `moi` | Multiplicity of infection |
+| `time_interval_min` | Minutes between frames |
+| `data_path` | Path to HCS OME-Zarr store (FOV-level — extract zarr root by trimming well/fov) |
+| `tracks_path` | Path to tracking zarr (may be absent) |
+| `channel_0_name` .. `channel_N_name` | Zarr channel names |
+| `channel_0_marker` .. `channel_N_marker` | Protein marker for each channel |
+| `t_shape`, `c_shape`, `z_shape`, `y_shape`, `x_shape` | Array dimensions |
+| `pixel_size_xy_um`, `pixel_size_z_um` | Physical pixel sizes |
+
+## Usage
+
+The user will describe what they want in natural language, e.g.:
+
+- "fetch the dataset from 2025_07_24 with all the organelles from that experiment"
+- "build a collection with the SEC61 and TOMM20 experiments from July 2025"
+- "make a collection for all ZIKV infection datasets"
+
+## Process
+
+### Step 1: Query Airtable
+
+Search for matching records using `mcp__airtable__list_records` with `filterByFormula`.
+
+Common filter patterns:
+- By dataset name: `SEARCH("2025_07_24", {dataset})`
+- By organelle: `{organelle} = "SEC61"`
+- By perturbation: `{perturbation} = "ZIKV"`
+- Combined: `AND(SEARCH("2025_07", {dataset}), {organelle} = "TOMM20")`
+
+Use `mcp__airtable__list_records` with `filterByFormula` for precise filtering.
+Use `mcp__airtable__search_records` for fuzzy text matching.
+
+### Step 2: Group and Summarize
+
+Group records by `dataset`. **If a single dataset contains multiple markers/organelles** (different `marker` values across wells), split it into one experiment entry per marker. The experiment name gets a `_{MARKER}` suffix (e.g. `2025_07_24_A549_SEC61_TOMM20_G3BP1_ZIKV_TOMM20`). All split entries share the same `data_path` and `tracks_path` but have different `perturbation_wells`, `marker`, and `organelle`.
+
+This is handled automatically by `build_collection()` in `packages/viscy-data/src/viscy_data/collection.py` via the `_group_records()` helper.
+
+Present a summary table to the user showing:
+
+- Dataset names found (with split entries if multi-organelle)
+- Number of FOVs per dataset
+- Organelles and markers
+- Channel names and markers
+- Conditions (inferred from `perturbation` field — see note below)
+- Wells per condition
+- Whether `tracks_path` is available
+
+**Note on cell_state**: In Airtable, `cell_state` is typically "Live" for all records. Infer infection status from the `perturbation` field: wells with a perturbation value are "infected", wells without are "uninfected".
+
+Ask the user to confirm which datasets to include.
+
+### Step 3: Determine Channels
+
+Each experiment entry has a `channels` list where each entry maps a zarr channel name to a protein marker:
+
+```yaml
+channels:
+  - name: "Phase3D"           # zarr channel name
+    marker: "Phase3D"         # protein marker / semantic label
+  - name: "raw GFP EX488 EM525-45"
+    marker: "SEC61B"
+```
+
+Rules for mapping:
+1. `channel_X_name` from Airtable → `name` field (the zarr channel name)
+2. `channel_X_marker` from Airtable → `marker` field (the protein marker)
+3. Only include channels relevant to the experiment — typically Phase3D (labelfree) and the fluorescence channel(s) for the marker of interest
+
+Present the proposed channel mapping to the user for confirmation:
+```
+Channels per experiment:
+  2025_07_24_SEC61:
+    - Phase3D → Phase3D
+    - raw GFP EX488 EM525-45 → SEC61B
+  2024_08_14_ZIKV:
+    - Phase3D → Phase3D
+    - MultiCam_GFP_BF → pAL10
+```
+
+### Step 4: Determine tracks_path
+
+Check the `tracks_path` field in Airtable. If missing, ask the user.
+
+### Step 5: Naming Convention
+
+Collection filenames follow: `{cell_line}_{perturbation}_{organelle}.yml`
+
+- **Single organelle**: use the organelle name, e.g. `A549_ZIKV_SEC61.yml`
+- **Multiple organelles**: use `multiorganelle`, e.g. `A549_ZIKV_multiorganelle.yml`
+- **No version suffix** — versioning is handled by git history
+- The `name` field inside the YAML should match the filename (without `.yml`)
+
+### Step 6: Generate Collection YAML
+
+Use the Collection schema from `packages/viscy-data/src/viscy_data/collection.py`.
+
+The current schema uses per-experiment `channels` (list of `{name, marker}` entries), NOT `source_channels`:
+
+```yaml
+name: <filename without .yml>
+description: "<what this collection contains>"
+
+provenance:
+  airtable_base_id: app8vqaoWyOwa0sB5
+  airtable_query: "<the filter formula used>"
+  record_ids: []
+  created_at: "<ISO 8601 timestamp>"
+  created_by: "<user name if known>"
+
+experiments:
+  - name: <dataset_name or dataset_marker split name>
+    data_path: <zarr store root — trim well/fov from airtable data_path>
+    tracks_path: <from airtable or user>
+    channels:
+      - name: <zarr_channel_name>
+        marker: <protein_marker>
+      - name: <zarr_channel_name>
+        marker: <protein_marker>
+    perturbation_wells:
+      uninfected:
+        - <well_id>
+      <perturbation_name>:
+        - <well_id>
+    interval_minutes: <time_interval_min>
+    start_hpi: <hours_post_perturbation or 0.0>
+    marker: <primary marker>
+    organelle: <organelle>
+    moi: <moi or 0.0>
+    pixel_size_xy_um: <from airtable>
+    pixel_size_z_um: <from airtable>
+```
+
+Key notes:
+- `data_path` should be the zarr store root (up to `.zarr`), NOT the FOV-level path from Airtable
+- `perturbation_wells` uses `uninfected` / `<perturbation>` keys inferred from the `perturbation` field
+- `channels` lists only the channels needed for training (not all channels in the zarr)
+- `marker` at the experiment level is the primary marker for this experiment entry
+
+### Step 7: Save and Validate
+
+1. Save to `applications/dynaclr/configs/collections/<name>.yml`
+2. Validate by loading with `viscy_data.collection.load_collection(path)` using a quick Python check
+3. Show the user the final YAML and validation result
+
+## Important Notes
+
+- `interval_minutes` must be > 0
+- `perturbation_wells` must not be empty
+- Zarr channel names in `channels[].name` must match actual zarr channel names
+- For multi-marker datasets, split into separate experiment entries per marker
+- Reference existing collections in `applications/dynaclr/configs/collections/` for format examples

.claude/skills/airtable-register/SKILL.md

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+---
+name: airtable-register
+description: Register zarr positions into the Computational Imaging Database on Airtable, write channels_metadata/experiment_metadata to zarr .zattrs, or bulk-update Airtable records via MCP. Use when the user asks to "register a dataset", "register zarr positions", "update airtable from zarr", "write metadata to zarr", "run register on", "sync airtable", "populate channel markers", "update airtable records", "backfill fields", or "fill in missing fields". Also use for Marker Registry questions.
+version: 3.0.0
+author: ai-x-imaging
+tags: [Airtable, OME-Zarr, Metadata, Registration, DynaCLR, VisCy]
+---
+
+# Airtable Registration & Update Skill
+
+Manages bidirectional metadata sync between OME-Zarr datasets and the Computational Imaging Database on Airtable, and supports bulk field updates via MCP.
+
+## Airtable Configuration
+
+- **Base ID**: `app8vqaoWyOwa0sB5` (Computational Imaging Database)
+- **Datasets table ID**: `tblaFzrDMlVZHPZIj`
+- **Collections table ID**: `tblu0Rbj9OnLl7vJf`
+- **Models table ID**: `tblVZhRA48tDMWj8U`
+- **Marker Registry table**: `tblmP8l2GmpCeERyD`
+- **Script**: `applications/airtable/scripts/write_experiment_metadata.py`
+- **Core logic**: `applications/airtable/src/airtable_utils/registration.py`
+- **Schemas**: `applications/airtable/src/airtable_utils/schemas.py`
+- **Database interface**: `applications/airtable/src/airtable_utils/database.py`
+- **Channel parsing**: `packages/viscy-data/src/viscy_data/channel_utils.py`
+- `MAX_CHANNELS = 8` (defined in `schemas.py`)
+- `AIRTABLE_API_KEY` and `AIRTABLE_BASE_ID` must be set in environment
+
+## Operations
+
+### 1. Register (zarr -> Airtable)
+
+Reads zarr metadata and writes per-FOV records to Airtable.
+
+**Fields written by register:**
+- `data_path` — full path to zarr position
+- `channel_{i}_name` — zarr channel names (up to 8)
+- `channel_{i}_marker` — protein marker, derived from Marker Registry
+- `t_shape`, `c_shape`, `z_shape`, `y_shape`, `x_shape` — array dimensions
+- `pixel_size_xy_um`, `pixel_size_z_um` — from zarr coordinate transforms
+
+**Marker derivation rules:**
+- **labelfree** channels -> marker = channel name (e.g. `"Phase3D"`, `"BF"`, `"DIC"`)
+- **virtual_stain** channels -> marker = channel name (e.g. `"nuclei_prediction"`)
+- **fluorescence** channels -> substring-match aliases against channel name -> protein marker from registry (e.g. `"TOMM20"`, `"SEC61B"`)
+
+#### Commands
+
+```bash
+# Dry run first (always recommended)
+uv run --package airtable-utils \
+    applications/airtable/scripts/write_experiment_metadata.py \
+    register --dry-run /path/to/dataset.zarr/*/*/*
+
+# Register all positions
+uv run --package airtable-utils \
+    applications/airtable/scripts/write_experiment_metadata.py \
+    register /path/to/dataset.zarr/*/*/*
+
+# Single position
+uv run --package airtable-utils \
+    applications/airtable/scripts/write_experiment_metadata.py \
+    register /path/to/dataset.zarr/A/1/000000
+
+# Override dataset name (when zarr stem doesn't match Airtable)
+uv run --package airtable-utils \
+    applications/airtable/scripts/write_experiment_metadata.py \
+    register --dataset my_dataset /path/to/dataset.zarr/*/*/*
+```
+
+#### Parquet Readiness Report
+
+After registration, the CLI prints a **Parquet Readiness** report that flags any fields still needed before a flat parquet cell index can be built. Fields are split by source:
+
+- **zarr** fields (auto-filled by `register`): `data_path`, `channel_N_name`, `channel_N_marker`, `pixel_size_xy_um`, `pixel_size_z_um`
+- **platemap** fields (biologist fills in Airtable): `tracks_path`, `perturbation`, `time_interval_min`, `hours_post_perturbation`, `cell_type`
+
+If any platemap fields are missing, the report shows what to fill in and how (Airtable UI or MCP bulk update).
+
+### 2. Write (Airtable -> zarr)
+
+Writes `channels_metadata` and `experiment_metadata` to zarr `.zattrs`.
+
+```bash
+uv run --package airtable-utils \
+    applications/airtable/scripts/write_experiment_metadata.py \
+    write /path/to/dataset.zarr/*/*/*
+```
+
+#### channels_metadata schema
+
+```json
+{
+  "Phase3D": {
+    "channel_type": "labelfree",
+    "biological_annotation": {"marker": "Phase3D"}
+  },
+  "raw GFP EX488 EM525-45": {
+    "channel_type": "fluorescence",
+    "biological_annotation": {
+      "marker": "TOMM20",
+      "marker_type": "protein_tag",
+      "fluorophore": null
+    }
+  }
+}
+```
+
+#### experiment_metadata schema
+
+```json
+{
+  "perturbations": [
+    {"name": "ZIKV", "type": "unknown", "hours_post": 48.0, "moi": 5.0}
+  ],
+  "time_sampling_minutes": 15.0
+}
+```
+
+### 3. Bulk Update (via Airtable MCP)
+
+For updating fields that don't come from zarr (e.g. `tracks_path`, `organelle`, manually-curated fields).
+
+**Process:**
+
+1. Fetch target records with `mcp__airtable__list_records` using `filterByFormula`
+2. Compute new values (python/jq)
+3. Batch update with `mcp__airtable__update_records` (max 10 per call, send all batches in parallel)
+4. Verify with the same filter query (must return zero remaining records)
+
+**Pagination warning:** `mcp__airtable__list_records` returns ~100 records max per call. If count equals ~100, re-query with tighter filters.
+
+## Datasets Table Fields
+
+| Field | Description | Written by register? |
+|---|---|---|
+| `dataset` | Experiment name | on create |
+| `well_id` | Well path (e.g. `B/2`) | on create |
+| `fov` | FOV identifier | on create |
+| `data_path` | Path to HCS OME-Zarr position | yes |
+| `tracks_path` | Path to tracking zarr | no (manual/MCP) |
+| `channel_0_name` .. `channel_7_name` | Zarr channel names | yes |
+| `channel_0_marker` .. `channel_7_marker` | Protein marker per channel | yes |
+| `t_shape` .. `x_shape` | Array dimensions | yes |
+| `pixel_size_xy_um` | Physical XY pixel size (um) | yes |
+| `pixel_size_z_um` | Physical Z pixel size (um) | yes |
+| `marker` | Well-level primary marker | template copy |
+| `organelle` | Target organelle | template copy |
+| `perturbation` | Perturbation applied | template copy |
+| `cell_type` | Cell type (e.g. `A549`) | template copy |
+| `cell_state` | Condition label | template copy |
+| `time_interval_min` | Minutes between frames | template copy |
+| `hours_post_perturbation` | HPI at imaging start | template copy |
+| `moi` | Multiplicity of infection | template copy |
+| `fluorescence_modality` | Imaging modality | template copy |
+
+## Marker Registry
+
+Table `tblmP8l2GmpCeERyD` — maps constructs to protein markers.
+
+| Field | Type | Example |
+|---|---|---|
+| `marker-fluorophore` | text (primary) | `TOMM20-GFP` |
+| `channel_name_aliases` | text | `GFP, FITC` |
+| `marker` | text | `TOMM20` |
+
+Matching is substring-based: `"GFP" in "raw GFP EX488 EM525-45"` -> match.
+
+## Flat Parquet Alignment
+
+The `register` command writes all fields needed to build a flat parquet cell index:
+- `channel_{i}_name` -> parquet `channel_name`
+- `channel_{i}_marker` -> parquet `marker`
+- `pixel_size_xy_um`, `pixel_size_z_um` -> parquet pixel size columns
+- `data_path` -> parquet `store_path`
+
+## Dataset Directory Conventions
+
+For **organelle_dynamics** datasets:
+```
+data_path:    /hpc/projects/intracellular_dashboard/organelle_dynamics/{EXP}/2-assemble/{EXP}.zarr
+tracks_path:  /hpc/projects/intracellular_dashboard/organelle_dynamics/{EXP}/1-preprocess/label-free/3-track/{EXP}_cropped.zarr
+```
+
+Other families (organelle_box, viral-sensor) have non-standard structures — check filesystem.
+
+## Example Invocations
+
+- "register this dataset /path/to/dataset.zarr/*/*/*"
+- "write metadata to zarr for dataset X"
+- "update tracks_path for all organelle_dynamics datasets"
+- "fill in pixel_size_xy_um for all records where it's missing"
+- "set organelle = 'mitochondria' for all 2024_11_21 records"