Develop, deploy, and maintain the DynaCell virtual-staining HuggingFace demo hosted at biohub/dynacell (ZeroGPU). Covers hosting in the biohub Dynacell resource group, the private-artifacts + HF_TOKEN access model, ZeroGPU configuration, and the deploy/smoke-test workflow. Use when the user asks to "push the dynacell space", "update the dynacell demo", "upload checkpoints", "deploy the hf space", mentions biohub/dynacell, or works in applications/dynacell/examples/hf_demo/.

2026-06-16102

airtable-register

mehta-lab/VisCy

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.

2026-06-04102

prepare-dataset

mehta-lab/VisCy

Prepare datasets for training on VAST storage (NFS -> VAST rechunked zarr v3 pipeline). Use when the user asks to "prepare a dataset", "run prepare", "rechunk dataset", "copy dataset to VAST", "run QC and preprocess", or references the `prepare` CLI.

2026-06-04102

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:

channels:
  - name: "Phase3D"           # zarr channel name
    marker: "Phase3D"         # protein marker / semantic label
  - name: "raw GFP EX488 EM525-45"
    marker: "SEC61B"

Rules for mapping:

channel_X_name from Airtable → name field (the zarr channel name)
channel_X_marker from Airtable → marker field (the protein marker)
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:

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

Save to applications/dynaclr/configs/collections/<name>.yml
Validate by loading with viscy_data.collection.load_collection(path) using a quick Python check
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