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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/lamm-mit-omero-integration
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About
Use cases: data, writing
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What's inside
- Skill name
omero-integration- Description
- Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
- License
- Apache-2.0
- Metadata
- skill-author: K-Dense Inc.
- Instructions
- 244 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (11)
- SKILL.md
- advanced.md · references/
- connection.md · references/
- data_access.md · references/
- image_processing.md · references/
- metadata.md · references/
- rois.md · references/
- scripts.md · references/
- tables.md · references/
- demo.cpython-313.pyc · scripts/__pycache__/
- demo.py · scripts/
SKILL.md (first lines)
# OMERO Integration ## Overview OMERO is an open-source platform for managing, visualizing, and analyzing microscopy images and metadata. Access images via Python API, retrieve datasets, analyze pixels, manage ROIs and annotations, for high-content screening and microscopy workflows. ## When to Use This Skill This skill should be used when: - Working with OMERO Python API (omero-py) to access microscopy data - Retrieving images, datasets, projects, or screening data programmatically - Analyzing pixel data and creating derived images - Creating or managing ROIs (regions of interest) on microscopy images - Adding annotations, tags, or metadata to OMERO objects - Storing measurement results in OMERO tables - Creating server-side scripts for batch processing - Performing high-content screening analysis ## Core Capabilities This skill covers eight major capability areas. Each is documented in detail in the references/ directory: ### 1. Connection & Session Management **File**: `references/connection.md` Establish secure connections to OMERO servers, manage sessions, handle authentication, and work with group contexts. Use this for initial setup and connection patterns. **Common scenarios:** - Connect to OMERO server with credentials - Use existing session IDs - Switch between group contexts - Manage connection lifecycle with context managers ### 2. Data Access & Retrieval **File**: `references/data_access.md` Navigate OMERO's hierarchical data structure (Projects → Datasets → Images) and screening data (Screens → Plates → Wells). Retrieve objects, query by attributes, and access metadata. **Common scenarios:** - List all projects and datasets for a user - Retrieve images by ID or dataset - Access screening plate data - Query objects with filters ### 3. Metadata & Annotations **File**: `references/metadata.md` Create and manage annotations including tags, key-value pairs, file attachments, and comments. Link annotations to images, datasets, or other objects. **Common scenarios:** - Add tags to images - Attach analysis results as files - Create custom key-value metadata - Query annotations by namespace ### 4. Image Processing & Rendering **File**: `references/image_processing.md` Access raw pixel data as NumPy arrays, manipulate rendering settings, create derived images, and manage physical dimensions. **Common scenarios:** - Extract pixel data for computational analysis - Generate thumbnail images - Create maximum intensity projections - Modify channel rendering settings ### 5. Regions of Interest (ROIs) **File**: `references/rois.md` Create, retrieve, and analyze ROIs with various shapes (rectangles, ellipses, polygons, masks, points, lines). Extract intensity statistics from ROI regions. **Common scenarios:** - Draw rectangular ROIs on images - Create polygon masks for segmentation - Analyze pixel intensities within ROIs - Export ROI coordinates ### 6. OMERO Tables **File**: `references/tables.md` Store and query structured tabular data associated with OMERO objects. Useful for analysis results, measurements, and metadata. **Common scenarios:** - Store quantitative measurements for images - Create tables with multiple column types - Query table data with conditions - Link tables to specific images or datasets ### 7. Scripts & Batch Operations **File**: `references/scripts.md` Create OMERO.scripts that run server-side for batch processing, automated workflows, and integration with OMERO clients. **Common scenarios:** - Process multiple images in batch - Create automated analysis pipelines - Generate summary statistics across datasets - Export data in custom formats ### 8. Advanced Features **File**: `references/advanced.md` Covers permissions, filesets, cross-group queries, delete operations, and other advanced functionality. **Common scenarios:** - Handle group permissions - Access original imported files - Perform cross-group queries - Delete objects with callbacks ## Installation ```bash uv pip install omero-py ``` **Requirements:** - Python 3.7+ - Zeroc Ice 3.6+
From lamm-mit/scienceclaw, Apache-2.0.
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Signals
- Stars
- 250
- Forks
- 47
- Open issues
- 1
- Last commit (this folder)
- Mar 14, 2026 (7 months ago)
- Last release
- Jun 23, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/lamm-mit/scienceclaw
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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omero-integration skill. Agent Mart. https://agentmart.dev/skills/lamm-mit-omero-integration (last updated 2026-10-24)