cobrapy

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Quality

73%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./skills/cobrapy/SKILL.md

Quality

Content

65%

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 code throughout and a distinctive, well-structured reference bundle, but the body is over-long and duplicates the workflows reference inline, and multi-step workflows lack embedded validation checkpoints.

Suggestions

Move the five inline 'Common Workflows' into references/workflows.md (which already covers them) and keep only a one-line pointer per workflow in the body to remove duplication and shrink token usage.

Embed explicit validation checkpoints inside batch/destructive workflows (e.g., assert solution.status == 'optimal' after optimize; validate samples before plotting) instead of collecting them in a separate Best Practices list.

Tighten the Overview paragraph by dropping marketing language ('essential for systems biology research', 'predict phenotypic behaviors') so it states only what the skill does.

Dimension	Reasoning	Score
Conciseness	Code blocks are lean and runnable, but the ~480-line body is padded by a marketing-flavored Overview ('essential for systems biology research', 'predict phenotypic behaviors') and five inline 'Common Workflows' that duplicate references/workflows.md.	2 / 3
Actionability	Every section provides fully executable, copy-paste-ready code with real imports (e.g., 'from cobra.flux_analysis import flux_variability_analysis') and concrete parameters, matching the 'fully executable code/commands' anchor.	3 / 3
Workflow Clarity	Workflows are sequenced linearly, but validation checkpoints ('Check solution status', 'Validate flux samples') live in a separate Best Practices list rather than embedded as validate→fix→retry loops; batch operations like double deletions and sampling lack explicit checkpoints, capping this at 2.	2 / 3
Progressive Disclosure	Two well-signaled one-level-deep references exist ('references/workflows.md', 'references/api_quick_reference.md') and are real files, but the body is near-monolithic and duplicates workflow content that should live only in the references, fitting the 'content that should be separate is inline' anchor.	2 / 3
	Total	9 / 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 strong, specific description that names concrete capabilities and occupies a clearly distinctive niche, but it lacks an explicit 'Use when…' trigger clause so the 'when to use' guidance is only implied.

Suggestions

Add an explicit 'Use when…' clause (e.g., 'Use when running FBA/FVA, gene knockout screens, or flux sampling on genome-scale metabolic models') to lift completeness to 3.

Spell out the acronyms once ('flux balance analysis (FBA), flux variability analysis (FVA)') to broaden natural trigger coverage for users who do not use the abbreviations.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'FBA, FVA, gene knockouts, flux sampling, SBML models' — rather than vague language, matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Clearly states what the skill does, but the 'when' is only implied by 'for systems biology and metabolic engineering analysis' with no explicit 'Use when…' trigger clause, which caps completeness at 2 per the guideline.	2 / 3
Trigger Term Quality	'FBA, FVA, gene knockouts, flux sampling, SBML models' are exactly the natural terms a systems biologist would say when needing this skill; the acronyms are the field's standard vocabulary.	3 / 3
Distinctiveness Conflict Risk	'Constraint-based metabolic modeling (COBRA)' is a sharply defined niche with distinct triggers that would not conflict with other skills.	3 / 3
	Total	11 / 12 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/cobrapy/SKILL.md
Commit: 757b63b

Reviewed: about 11 hours ago

Table of Contents

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