Agent skill · Data & Analytics

omero-integration

Securely inspect and automate microscopy data workflows against OMERO.server with omero-py, BlitzGateway, OMERO CLI, tables, annotations, ROIs, rendering, and documented OMERO.web APIs. Use for scoped OMERO inventory, metadata export, import/export planning, or reviewed write workflows.

K-Dense-AIgithub.com/K-Dense-AIGitHub ↗
claude-codeships scriptsMIT
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill omero-integration --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 15
SKILL.md size: 9 KB
Bundled scripts: yes
Version: 1.3
Requires: Requires network access to a user-selected OMERO.server for remote operations. The 2026-07-23 snapshot uses OMERO.py…
Path: skills/omero-integration/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 32,619
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# OMERO Integration Use current OME documentation and the smallest explicit data scope. OMERO data may contain unpublished images, identifiers, annotations, original files, and derived measurements. ## Verified Baseline This skill was refreshed on **2026-07-23**: - **OMERO.server 5.6.18** (May 2026) is the current documented stable server. - It was tested by OME with **OMERO.py/omero-py 5.22.1** and **OMERO.web 5.31.0**. - `omero-py==5.22.1` requires Python 3.10 or newer. The OMERO support matrix supports 3.10 and 3.11, recommends 3.12, and still labels 3.13/3.14 “upcoming.” - OMERO 5.6 uses **IcePy 3.6**, with 3.6.5 prebuilt client wheels documented for Python versions through 3.12. The pin above is a reproducible skill snapshot, not a promise that every OMERO.server release accepts that client. For another server version, consult its release entry and use the OMERO.py version tested with it. See [`references/sources.md`](references/sources.md). ## Operating Contract 1. Start with local validation or a dry run. Do not connect until the user has selected the host, group, object type, IDs, and result limit. 2. Read credentials only from the named `OMERO_*` variables in the frontmatt

What's inside
Steps it walks through
  1. Verified Baseline
  2. Operating Contract
  3. Choose the Interface
  4. Install a Reproducible Client
  5. Credentials and Connection
  6. Bundled Safe Helpers
  7. Capability Guide
  8. Final Review Before Remote Work
Ships with 14 files
  • references/advanced.md
  • references/connection.md
  • references/data_access.md
  • references/image_processing.md
  • references/metadata.md
  • references/rois.md
  • references/scripts.md
  • references/sources.md
  • references/tables.md
  • scripts/export_image_metadata.py
  • scripts/inventory.py
  • scripts/omero_common.py
  • scripts/plan_transfer.py
  • scripts/validate_config.py
Commands it runs
uv venv --python 3.12 .venv
source .venv/bin/activate
Download the matching 3.6.5 wheel from the official OMERO-linked matrix.
uv pip install "/absolute/path/to/zeroc_ice-3.6.5-<matching-tags>.whl"
uv pip install "omero-py==5.22.1"
export OMERO_HOST="omero.example.org"
export OMERO_PORT="4064"
export OMERO_USER="researcher"
export OMERO_SECURE="true"
Supply OMERO_PASSWORD through the environment/secret manager, or use
More from scientific-agent-skills
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About this skill
What does the omero-integration skill do?

Securely inspect and automate microscopy data workflows against OMERO.server with omero-py, BlitzGateway, OMERO CLI, tables, annotations, ROIs, rendering, and documented OMERO.web APIs. Use for scoped OMERO inventory, metadata export, import/export planning, or reviewed write workflows.

How do I install it?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill omero-integration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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