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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

68%

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tessl review fix ./backend/cli/skills/biology/omero-integration/SKILL.md

Quality

Content

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

The skill is well-structured as a navigable index over an eight-file reference bundle, with executable quick-start and error-handling code. Its main weaknesses are redundant routing/overview prose within the body and the absence of validation checkpoints in its batch-processing workflows.

Suggestions

Consolidate the triple routing ('Core Capabilities' scenarios, 'Selecting the Right Capability', and 'Common Workflows') into one routing section and drop the verbatim description restatement from the Overview to tighten conciseness.

Add validation/verification steps to the batch workflows (e.g. verify ROI counts or table rows written per image, and check for per-image failures before proceeding) to lift workflow clarity past the batch-operation cap of 3.

Make the Quick Start fully copy-paste ready by defining placeholder variables (username, password, host, port) or showing environment-variable/argument sourcing.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient, but the Overview restates the frontmatter description verbatim, the reference routing is repeated across 'Core Capabilities', 'Selecting the Right Capability', and 'Common Workflows', and 'Notes' repeats facts already demonstrated in code — matching anchor 3's 'some unnecessary explanation or could be tightened'.	3 / 5
Actionability	Concrete, mostly copy-paste-ready guidance is provided: an install command ('uv pip install omero-py'), executable BlitzGateway connection, context-manager, and try/except/finally error-handling snippets. Not 5 because quick-start code uses undefined placeholders (username, password, host) and omits common query patterns found in the references.	4 / 5
Workflow Clarity	Three workflows are clearly numbered with per-step reference pointers, but 'Workflow 2: Batch ROI Analysis' and the server-side script workflow involve batch operations with no validation or verification checkpoints, so workflow clarity is capped at 3 per the judging guidelines.	3 / 5
Progressive Disclosure	The body is a pure overview that defers all detail to eight well-signaled, one-level-deep references ('File: references/connection.md' etc.), all of which exist on disk, with routing guidance for both task types and workflows — matching anchor 5 exactly.	5 / 5
	Total	15 / 20 Passed

Description

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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 communicates a clear set of concrete capabilities in an appropriately terse style, but it never names OMERO — the single most important trigger term — and lacks any explicit 'Use when...' guidance. Adding both would move it to the top of the scale.

Suggestions

Include the product name as a trigger term, e.g. 'OMERO microscopy data management platform' or mention 'omero-py', since users will naturally say 'OMERO' when they need this skill.

Add an explicit 'when' clause such as 'Use when working with OMERO servers, omero-py, or microscopy image data' to raise completeness above the cap of 3.

Mention remaining capabilities (server connection/session management, storing measurements in OMERO tables) to close the specificity gap.

Dimension	Reasoning	Score
Specificity	The description lists several concrete actions ('Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing') with only minor gaps (connection/session management and OMERO-table storage are not mentioned), matching anchor 4 rather than 5's comprehensive coverage.	4 / 5
Completeness	It has a clear 'what' (microscopy data management with listed capabilities) but no 'Use when...' clause; 'for high-content screening and microscopy workflows' only weakly implies 'when', so completeness is capped at 3 per the judging guidelines.	3 / 5
Trigger Term Quality	Natural domain terms like 'microscopy', 'high-content screening', 'datasets', 'ROIs', and 'annotations' are present, but the primary trigger a user would actually say — 'OMERO' or 'omero-py' — is entirely absent, which is more than the 'a few natural terms missing' of anchor 4.	3 / 5
Distinctiveness Conflict Risk	Terms like 'ROIs', 'high-content screening', and 'microscopy data management platform' carve a mostly distinct niche, but omitting 'OMERO' leaves it generic enough for minor overlap with other microscopy-imaging skills.	4 / 5
	Total	14 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/omero-integration/SKILL.md
Revision: 6a85742

Reviewed: 22 days ago

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omero-integration

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