Open-source bio-image data management. Use the omero-py client to connect to an OMERO server, retrieve images as numpy arrays, annotate with tags and key-value pairs, manage ROIs, and feed image data into Python analysis pipelines — programmatically, no GUI.
SkillScore91Maintained
Automatic
◆Research pack
Last verified 8/23/2026
What it changes
Passed 20 of 22 test cases with the skill, and 17 of 22 without it.
Without skill77%
With skill91%
Checks it passed that the model alone missed
The function filters annotations by checking if OMERO_TYPE equals 'TagAnnotation'.
The function executes searchObjects with 'Image' specified as the target object type.
The environment setup instructions specify installing omero-py using conda rather than pip.
3 fixed17 passed either way2 still failing0 broke
Pass-rate lift
+14 pp
Tokens
+172%
Agent turns
0%
Response time
-31%
Model gemini-3.6-flashMeasured 8/23/2026v120 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 3
Prompt
Write a Python function that searches an OMERO server database for images matching the text query 'GFP_control' and prints their ID and name.
What was checked, without and with the skill
Checked
Without skill
With skill
The function executes searchObjects with 'Image' specified as the target object type.
Missed
Passed
case-05 · gemini-3.6-flash · 8/23/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md418 linessingle file
omero-integration
Overview
OMERO is an open-source image data management system widely used in microscopy facilities and core labs. The omero-py library provides a Python client (BlitzGateway) that connects to an OMERO server, allowing programmatic access to images, datasets, projects, tags, annotations, and ROIs. Use it to build automated analysis workflows that pull images from OMERO, process them in Python, and write results back as annotations.
When to Use
Programmatic image retrieval from OMERO: Downloading microscopy images as numpy arrays for downstream analysis without using the OMERO Insight GUI.
Bulk annotation and tagging: Applying tags, key-value pair annotations, or comments to large numbers of images/datasets based on analysis results.
ROI access and management: Reading segmentation ROIs (shapes) stored in OMERO for downstream quantification or export.
Integrating OMERO into Python analysis pipelines: Connecting OMERO image data to scikit-image, OpenCV, CellPose, or other image analysis tools.
Automated QC workflows: Querying images by metadata (channel, acquisition date, experimenter) and flagging those that fail quality criteria.
Data provenance tracking: Attaching analysis provenance (parameters, tool versions) as structured key-value annotations to images.
For local image analysis without an OMERO server, use tifffile, aicsimageio, or imageio directly.
Prerequisites
Python packages: omero-py, numpy, Pillow
System: Java 8+ (required by omero-py internals), Ice 3.6 (installed automatically via conda)
Data requirements: Access credentials to a running OMERO server (host, port, username, password)
Environment: Conda is strongly recommended; omero-py has complex dependencies