omero-integration

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

Quality

70%

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tessl review fix ./bundled/skills/omero-integration/SKILL.md

Quality

Content

72%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill body is well-structured with executable connection code and exemplary progressive disclosure across eight real reference files. Its main weaknesses are redundancy across the Overview/When-to-Use/Workflows sections and the absence of explicit validation checkpoints in the batch workflows.

Suggestions

Remove redundancy: drop or merge the Overview (it repeats the description) and fold the "When to Use" bullets into the Core Capabilities scenario lists they duplicate.

Add explicit validation checkpoints to the batch workflows — e.g. verify the connection succeeded and confirm images/ROIs were retrieved before iterating over them.

Consolidate "Selecting the Right Capability" with "Common Workflows" since both serve the same navigation purpose with overlapping content.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient (pointers to references, no deep concept explanations), but the Overview duplicates the description, the "When to Use" bullets restate the Core Capabilities scenarios, and "Selecting the Right Capability" overlaps "Common Workflows" — several sections could be tightened.	2 / 3
Actionability	It provides fully executable, copy-paste-ready code for connection (BlitzGateway + context manager), a working error-handling block, and a concrete install command, matching the score-3 anchor for executable examples with specific guidance.	3 / 3
Workflow Clarity	The three Common Workflows are clearly numbered and reference the right files, but they lack explicit validation checkpoints (e.g. verify connection, confirm images/ROIs retrieved) — and since Workflows 2 and 3 are batch operations, the guidelines cap workflow clarity at 2.	2 / 3
Progressive Disclosure	The body is a clean overview pointing to eight one-level-deep reference files, each signaled with "File: references/xxx.md"; all referenced files exist on disk, content is appropriately split, and "Selecting the Right Capability" plus "Common Workflows" provide easy navigation.	3 / 3
	Total	10 / 12 Passed

Description

67%Weight 40%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and well-scoped to microscopy data management, but it lacks an explicit Use-when trigger clause and, critically, never names OMERO/omero-py, the primary keyword users would actually invoke. These gaps keep completeness and trigger quality at the mid level.

Suggestions

Add an explicit trigger clause, e.g. "Use when working with OMERO servers, the omero-py API, or microscopy image data stored in OMERO."

Include the platform name "OMERO" and "omero-py" as primary trigger keywords so the skill matches what users naturally say.

Tighten the trailing "for high-content screening and microscopy workflows" into the Use-when clause to make the trigger explicit rather than implied.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions — "Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing" — matching the score-3 anchor for several specific concrete actions rather than a single vague domain statement.	3 / 3
Completeness	It clearly states what the skill does, but the "when" is only weakly implied by the trailing phrase "for high-content screening and microscopy workflows" rather than an explicit "Use when..." clause, which the guidelines say caps completeness at 2.	2 / 3
Trigger Term Quality	It includes relevant natural terms (microscopy, datasets, pixels, ROIs, high-content screening) but omits the platform name "OMERO" / "omero-py", the single most common keyword a user would say when needing this skill, so coverage is partial rather than complete.	2 / 3
Distinctiveness Conflict Risk	The niche is clear — microscopy data management with OMERO-style operations (ROIs, annotations, screening plates) — making it unlikely to trigger for unrelated skills despite the platform name being absent.	3 / 3
	Total	10 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/omero-integration/SKILL.md
Commit: f627ab5

Reviewed: 9 days ago

Table of Contents

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