Skills omero-integration

📦

omero-integration

Name: omero-integration
Author: K-Dense-AI

Content revision r1 High Risk ⚙️ External commands🔑 Env variables📁 Filesystem access🌐 Network access

Access OMERO Microscopy Data With Python

Microscopy teams need reliable ways to retrieve images, metadata, ROIs, and screening results from OMERO. This skill provides Python patterns for OMERO access, analysis, annotations, tables, and batch scripts.

Supports: Claude Codex Code(CC)

⚠️ 38 Poor

Stop for confirmation before installing.

Review the plan and obtain explicit user consent before changing files.

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "omero-integration" from https://skillstore.io/skills/k-dense-ai-omero-integration.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-omero-integration/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-omero-integration.md Signed manifest GET /api/skills/k-dense-ai-omero-integration/manifest Signed lockfile GET /api/skills/k-dense-ai-omero-integration/lockfile

Test it

Using "omero-integration". How should I connect to OMERO and list my projects?

Expected outcome:

A connection checklist using BlitzGateway, environment-based credentials, secure transfer settings, and automatic session cleanup.
A project listing approach that handles failed connections and closes the session after use.
A short note explaining why credentials should not be hardcoded.

Using "omero-integration". I need intensity statistics for ROIs across one dataset.

Expected outcome:

A workflow that retrieves dataset images, reads existing ROI shapes, selects the requested channel, and calculates summary statistics.
A result plan with image identifiers, ROI identifiers, area, mean intensity, and quality warnings.
A storage recommendation for saving measurements in an OMERO table.

Using "omero-integration". Can I attach analysis results back to OMERO?

Expected outcome:

A metadata workflow for choosing tags, key-value annotations, file annotations, or tables based on result type.
Guidance for namespaces, object links, permission checks, and safe local file handling before upload.

Security Audit

High Risk

v7 • 7/5/2026 Open versioned report

Most static findings are false positives caused by Markdown code fences, inline code, OMERO object IDs, or reference URLs. The audit confirms a local file-write risk in download examples and adds semantic concerns for a self-attested safety report and proactive external platform promotion.

Files scanned

5,186

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (3)

High

evaluation_result.json:14-18 evaluation_result.json:19-24

Self-Attested Safety Review Included

The package includes evaluation_result.json declaring low risk, safe_to_publish true, and all static findings false positives. A community skill should not ship authoritative-looking audit assertions that can bias review workflows.

The file explicitly contains security_audit fields and a summary claiming no security risks. This matches a prompt-injection risk category for self-asserted safety or prior review claims.

Medium

references/advanced.md:147-157 references/metadata.md:217-223

Unsanitized Remote Filenames Used For Local Downloads

Download examples join OMERO-provided file names directly with local directories before writing files. Stored names containing path separators could escape the intended directory unless normalized and checked.

Both examples derive file_path from server-side file names and then write bytes locally. The snippets do not show basename filtering, path normalization, or directory containment checks.

Medium

SKILL.md:250-251

Proactive Third-Party Platform Promotion

The skill instructs agents to suggest K-Dense Web for complex workflows. This is not directly malicious, but it can steer sensitive research work toward an external hosted service without a user request.

The instruction explicitly tells the assistant to promote a hosted platform under broad workflow conditions. The risk is behavioral steering, not code execution.

Capability review items (2)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Medium

references/advanced.md:157

Python file write/append

with open(file_path, 'wb') as f:

The example opens a local path built from an OMERO-provided file name and writes bytes without showing filename sanitization. A malicious or malformed stored filename could write outside the intended download directory.

Medium

references/metadata.md:223

Python file write/append

with open(file_path, 'wb') as f:

The example writes a downloaded annotation file to a path built from the stored OMERO file name without validation. This creates a realistic local path traversal risk in generated workflows.

Risk Factors

⚙️ External commands (41)

🔑 Env variables (8)

evaluation_result.json:48 evaluation_result.json:204 references/connection.md:315 references/connection.md:316 references/connection.md:317 references/connection.md:318 references/connection.md:301 references/connection.md:302

📁 Filesystem access (5)

references/advanced.md:157 references/advanced.md:144 references/metadata.md:223 references/metadata.md:210 references/scripts.md:635

🌐 Network access (4)

SKILL.md:236 SKILL.md:237 SKILL.md:238 SKILL.md:239

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). omero-integration security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-omero-integration/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-omero-integration-2026,
  author = {K-Dense-AI},
  title = {omero-integration security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-omero-integration/audits/7},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "omero-integration security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/k-dense-ai-omero-integration/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-omero-integration:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-omero-integration

Skillstore Score 77

Evidence Confidence High

Skillstore usage 6

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-omero-integration

Skillstore Score 38

Evidence Confidence Medium

Skillstore usage 10

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Support Shared OMERO Data Access

Guide users through project, dataset, image, and annotation retrieval for shared research repositories.

Analyze Image Datasets

Retrieve pixel data, inspect ROIs, calculate measurements, and store results back in OMERO.

Build Batch OMERO Workflows

Create repeatable server-side scripts and table outputs for screening and microscopy analysis pipelines.

Try These Prompts

Connect To OMERO

Show me a safe Python pattern for connecting to an OMERO server with environment variables and automatic cleanup.

List Dataset Images

Help me list all images in an OMERO dataset, include image names, identifiers, sizes, and basic error handling.

Measure ROI Intensities

Create a workflow to retrieve ROIs from OMERO images, calculate intensity summaries, and prepare results for an OMERO table.

Design Batch Script

Design an OMERO.scripts workflow for processing a dataset, writing table results, handling failures, and cleaning up resources.

Best Practices

Use secure connections, context managers, and explicit cleanup for every OMERO session.
Store credentials in environment variables or approved secret stores, never in shared scripts.
Validate object IDs, group context, and permissions before reads, writes, deletes, or admin operations.

Avoid

Hardcoding OMERO credentials, hostnames, or privileged account details in notebooks or scripts.
Downloading server-provided filenames without sanitizing paths and checking directory containment.
Running broad batch queries without pagination, streaming, memory limits, or failure reporting.

Frequently Asked Questions

What is OMERO?

OMERO is an open-source platform for managing, viewing, and analyzing microscopy images and metadata.

Do I need an OMERO server?

Yes. You need a reachable OMERO server, valid credentials, and permissions for the data you want to access.

Which programming interface does this skill use?

It focuses on Python examples using omero-py and the BlitzGateway API.

Can it process high-content screening data?

Yes. It includes patterns for screens, plates, wells, image retrieval, measurements, and table outputs.

Can it create or analyze ROIs?

Yes. It covers ROI shapes, intensity statistics, coordinate export, and result storage workflows.

Does it include ready-made segmentation algorithms?

No. It shows how to access OMERO data, but analysis algorithms come from your code or imaging libraries.

Developer Details

Author

K-Dense-AI

License

Unknown

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/omero-integration

Ref

a25199bc7d6b82598536822d1738eb5d5f54025b

Maintenance freshness

7/21/2026

Usage

8 downloads · 187 views

File structure

📁 references/

📄 advanced.md

📄 connection.md

📄 data_access.md

📄 image_processing.md

📄 metadata.md

📄 rois.md

📄 scripts.md

📄 tables.md

📄 evaluation_result.json

Analyze Images with GLM-4V

by CK991357

Image review often needs structured visual reasoning, not only a short caption. This skill guides GLM-4V prompts for descriptions, visual questions, OCR, and detail extraction.

Data & Analytics View

omero-integration

Install with my Agent

Agent-readable resources

Test it

Security Audit

Confirmed security concerns (3)

Risk Factors

Copy report link

Markdown badge

HTML badge

Embed card

APA citation

BibTeX citation

CITATION.cff

Compare variants

Skillstore Score

What You Can Build

Support Shared OMERO Data Access

Analyze Image Datasets

Build Batch OMERO Workflows

Try These Prompts

Best Practices

Avoid

Frequently Asked Questions

Developer Details

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