cobrapy

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

1.30x

Quality

73%

Does it follow best practices?

Impact

85%

1.30x

Average score across 6 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/cobrapy/SKILL.md

Quality

Discovery

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.

This is a strong, domain-specific description that effectively lists concrete capabilities and uses natural terminology that practitioners in systems biology would recognize. Its main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know precisely when to select this skill. The specificity and distinctiveness are excellent for a specialized scientific domain.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about metabolic modeling, flux balance analysis, constraint-based reconstruction, or working with SBML/genome-scale models.'

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: FBA, FVA, gene knockouts, flux sampling, SBML models. These are well-defined technical operations in the metabolic modeling domain.	3 / 3
Completeness	Clearly answers 'what' (constraint-based metabolic modeling with FBA, FVA, gene knockouts, etc.) but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric.	2 / 3
Trigger Term Quality	Includes strong natural keywords users in this domain would use: 'COBRA', 'FBA', 'FVA', 'gene knockouts', 'flux sampling', 'SBML', 'metabolic modeling', 'systems biology', 'metabolic engineering'. Good coverage of both acronyms and descriptive terms.	3 / 3
Distinctiveness Conflict Risk	Highly specialized niche in constraint-based metabolic modeling with distinct domain-specific triggers (COBRA, FBA, FVA, SBML). Very unlikely to conflict with other skills.	3 / 3
	Total	11 / 12 Passed

Implementation

64%

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

This is a well-structured and highly actionable skill with excellent executable code examples covering the full breadth of COBRApy functionality. Its main weaknesses are its length (could be more concise by moving detailed sections to reference files) and the lack of validation checkpoints in workflows. The content would benefit from trimming explanatory prose and adding error-handling guidance.

Suggestions

Move detailed sections (e.g., Model Building, Gapfilling, Production Envelopes) to reference files and keep only the most common operations (FBA, FVA, knockouts, media) in the main skill to improve conciseness and progressive disclosure.

Add validation checkpoints to workflows, e.g., checking solution.status == 'optimal' before accessing fluxes, and include error recovery guidance for common failure modes.

Fix Workflow 2 where 'solution.objective_value' is referenced but 'solution' is never defined in that workflow block.

Trim introductory prose like the Overview paragraph and section lead-ins (e.g., 'COBRApy provides comprehensive tools organized into several key areas') that don't add actionable information.

Dimension	Reasoning	Score
Conciseness	The skill is fairly comprehensive but includes some unnecessary explanatory text (e.g., 'essential for systems biology research', 'behaving like both lists and dictionaries') and could be tightened. The overview paragraph and some section intros add tokens without adding value for Claude. However, most content is code-focused and reasonably efficient.	2 / 3
Actionability	Excellent actionability throughout - nearly every section contains complete, executable Python code examples with specific function calls, import statements, and realistic parameters. Code is copy-paste ready with concrete model names, reaction IDs, and expected outputs.	3 / 3
Workflow Clarity	The five named workflows provide clear sequences, but they lack validation checkpoints and error handling. For example, Workflow 2 references 'solution.objective_value' without having defined 'solution' in that workflow. No feedback loops for error recovery are present, and there's no guidance on what to do when optimization fails or returns infeasible results.	2 / 3
Progressive Disclosure	References to external files (references/workflows.md, references/api_quick_reference.md) are present at the bottom, but the main file is quite long (~300+ lines) with extensive inline content that could be split. The 10 core capability sections plus 5 workflows plus key concepts plus best practices makes this a dense monolithic document that would benefit from better separation.	2 / 3
	Total	9 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

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

	Total	10 / 11 Passed

Repository: K-Dense-AI/claude-scientific-skills
Commit: 25e1c0f

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.