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Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Basierend auf der SOC-Berufsklassifikation
| 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 | Unknown |
| required_environment_variables | [{"name":"OMERO_HOST","prompt":"OMERO server hostname.","required_for":"full functionality"},{"name":"OMERO_PORT","prompt":"OMERO server port (default 4064).","required_for":"optional features"},{"name":"OMERO_USER","prompt":"OMERO username.","required_for":"full functionality"},{"name":"OMERO_PASSWORD","prompt":"OMERO password.","required_for":"full functionality"}] |
| metadata | {"version":"1.1","skill-author":"K-Dense Inc.","openclaw":{"envVars":[{"name":"OMERO_HOST","required":true,"description":"OMERO server hostname."},{"name":"OMERO_PORT","required":false,"description":"OMERO server port (default 4064)."},{"name":"OMERO_USER","required":true,"description":"OMERO username."},{"name":"OMERO_PASSWORD","required":true,"description":"OMERO password."}]}} |
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.
This skill should be used when:
This skill covers eight major capability areas. Each is documented in detail in the references/ directory:
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:
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:
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:
File: references/image_processing.md
Access raw pixel data as NumPy arrays, manipulate rendering settings, create derived images, and manage physical dimensions.
Common scenarios:
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:
File: references/tables.md
Store and query structured tabular data associated with OMERO objects. Useful for analysis results, measurements, and metadata.
Common scenarios:
File: references/scripts.md
Create OMERO.scripts that run server-side for batch processing, automated workflows, and integration with OMERO clients.
Common scenarios:
File: references/advanced.md
Covers permissions, filesets, cross-group queries, delete operations, and other advanced functionality.
Common scenarios:
uv pip install omero-py
Requirements:
Basic connection pattern:
from omero.gateway import BlitzGateway
# Connect to OMERO server
conn = BlitzGateway(username, password, host=host, port=port)
connected = conn.connect()
if connected:
# Perform operations
for project in conn.listProjects():
print(project.getName())
# Always close connection
conn.close()
else:
print("Connection failed")
Recommended pattern with context manager:
from omero.gateway import BlitzGateway
with BlitzGateway(username, password, host=host, port=port) as conn:
# Connection automatically managed
for project in conn.listProjects():
print(project.getName())
# Automatically closed on exit
For data exploration:
references/connection.md to establish connectionreferences/data_access.md to navigate hierarchyreferences/metadata.md for annotation detailsFor image analysis:
references/image_processing.md for pixel data accessreferences/rois.md for region-based analysisreferences/tables.md to store resultsFor automation:
references/scripts.md for server-side processingreferences/data_access.md for batch data retrievalFor advanced operations:
references/advanced.md for permissions and deletionreferences/connection.md for cross-group queriesreferences/connection.md)references/data_access.md)references/data_access.md)references/image_processing.md)references/tables.md or references/metadata.md)references/rois.md)references/rois.md)references/tables.md)references/scripts.md)Always wrap OMERO operations in try-except blocks and ensure connections are properly closed:
from omero.gateway import BlitzGateway
import traceback
try:
conn = BlitzGateway(username, password, host=host, port=port)
if not conn.connect():
raise Exception("Connection failed")
# Perform operations
except Exception as e:
print(f"Error: {e}")
traceback.print_exc()
finally:
if conn:
conn.close()
Deterministically query 78 public scientific, biomedical, materials science, regulatory, finance, and demographics databases through documented REST APIs. Use for reproducible lookups of compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, structures, astronomy objects, environmental records, or database-backed scientific facts when endpoints, filters, pagination, and provenance need to be explicit.
Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pipelines, tokenizers, or TrainingArguments—not for general ML outside the Transformers library.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Complete mass spectrometry analysis platform. Use for proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free and isobaric quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistica