bioimage-analysis

Microscopy image analysis for cell biology. Cell segmentation (Cellpose, watershed), object tracking (trackpy), morphology quantification, colony counting, colocalization analysis, and cytoskeleton characterization. For pathology WSI use pathml; for flow cytometry use flow-cytometry-analysis.

Quality

70%

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Securityby

Passed

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tessl review fix ./backend/cli/skills/biology/bioimage-analysis/SKILL.md

Quality

Content

57%

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

Highly actionable with executable, copy-paste-ready code, but the body is overlong with redundant duplication between Core Capabilities and Typical Workflows, and it ignores the existing scripts/ bundle entirely rather than pointing to it. It also lacks explicit validation feedback loops for batch operations.

Suggestions

Reference the existing scripts/ files (segment_cells.py, track_cells.py, count_colonies.py, analyze_morphology.py, colocalization.py) instead of inlining their code, so SKILL.md becomes a lean overview with one-level-deep references.

Add explicit validation checkpoints to batch workflows, e.g. a 'validate segmentation on a sample image before batch processing' step with a retry loop.

Remove the duplication between the Core Capabilities code blocks and the Typical Workflows section by keeping one canonical example per task.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with executable code blocks that earn their place, but the ~540-line body restates the description in the Overview and duplicates colony counting (Capability #5 vs Workflow 4) and colocalization (Capability #6 vs Workflow 3), so it could be tightened; not the lean 'every token earns its place' top anchor.	2 / 3
Actionability	Provides fully executable, copy-paste-ready code throughout — e.g. the Cellpose eval call with concrete parameters and `skimage.measure.regionprops_table` with explicit property lists — matching the top anchor of fully executable guidance.	3 / 3
Workflow Clarity	The 'Typical Workflows' are presented as single monolithic scripts without numbered steps or explicit validation checkpoints, and batch processing (Workflow context) lacks a validate→fix→retry feedback loop; per the rubric, missing validation in batch operations caps workflow clarity at 2.	2 / 3
Progressive Disclosure	The SKILL.md is a monolithic 540-line document and never references the five relevant bundle scripts in scripts/ (e.g. segment_cells.py, track_cells.py, count_colonies.py, analyze_morphology.py, colocalization.py) that duplicate the inline code, so content that should be split is inline with no file-based navigation — matching the bottom anchor of poor organization.	1 / 3
	Total	8 / 12 Passed

Description

82%

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

A specific, well-scoped description with concrete capabilities and good natural trigger terms, but it is missing an explicit 'Use when...' clause that would fully answer when to invoke it. Adding a positive trigger sentence would raise completeness to 3.

Suggestions

Add an explicit 'Use when...' trigger clause, e.g. 'Use when analyzing microscopy images for cell segmentation, tracking, morphology, colony counting, or colocalization.'

Include a few common user phrasings such as 'count cells' or 'cell tracking' to broaden natural trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Cell segmentation (Cellpose, watershed), object tracking (trackpy), morphology quantification, colony counting, colocalization analysis, and cytoskeleton characterization' — matching the top anchor of several specific concrete actions.	3 / 3
Completeness	It clearly states what the skill does but lacks an explicit 'Use when...' trigger clause; the only 'when' guidance is negative scoping ('For pathology WSI use pathml'), so per the judging guidelines a missing explicit trigger caps completeness at 2.	2 / 3
Trigger Term Quality	Covers natural terms a user would say such as 'microscopy image analysis', 'cell segmentation', 'colony counting', and 'colocalization analysis', giving good coverage rather than just jargon; not penalized below 3 as it lacks only minor variations.	3 / 3
Distinctiveness Conflict Risk	Clear niche (microscopy image analysis for cell biology) with explicit negative-scoping redirects to pathml and flow-cytometry-analysis, making it unlikely to trigger for the wrong skill.	3 / 3
	Total	11 / 12 Passed

Validation

81%

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 — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (548 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/bioimage-analysis/SKILL.md
Commit: 52845c3

Reviewed: about 10 hours ago

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