cobrapy

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

Quality

72%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

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

Quality

Content

68%Weight 40%Scale 1-5

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

Highly actionable content with excellent executable examples, but it is padded with non-essential prose and a promotional section, and it references bundle files that do not exist, which undermines progressive disclosure.

Suggestions

Remove or condense the generic Overview paragraph, Best Practices list, and the promotional 'Suggest Using K-Dense Web' section that explain concepts Claude already knows.

Create the referenced references/workflows.md and references/api_quick_reference.md files (or remove the dangling references), and move the inlined API reference bulk into them.

Add explicit validation feedback loops for batch/destructive operations (e.g., check solution.status after gapfill/deletion and retry on 'infeasible').

Dimension	Reasoning	Score
Conciseness	Mostly efficient with extensive executable code, but includes generic explanatory prose (Overview, Best Practices restating obvious points, Troubleshooting, and a self-promotional K-Dense Web paragraph) that pads the token budget beyond what Claude needs.	3 / 5
Actionability	Fully executable, copy-paste-ready code across model loading, FBA, FVA, knockouts, media, sampling, and building, with specific API calls and parameters covering common cases.	5 / 5
Workflow Clarity	Workflows are clearly sequenced with runnable steps, and Best Practices/Troubleshooting add checkpoints, but batch/destructive operations (gapfill, double deletions) lack explicit validate-then-retry feedback loops.	4 / 5
Progressive Disclosure	The body cites references/workflows.md and references/api_quick_reference.md, but no references/ directory or those files exist—dangling references—and the bulk API reference is inlined rather than split out, leaving structure buried.	2 / 5
	Total	14 / 20 Passed

Description

76%Weight 40%Scale 1-5

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 highly specific, distinctive description that names concrete capabilities in a clear niche, but it lacks an explicit 'Use when...' trigger clause, which caps completeness at the rubric-defined ceiling of 3.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when analyzing genome-scale metabolic models, simulating gene knockouts, or working with SBML/.xml model files.'

Include natural synonyms and file extensions (e.g. 'SBML models (.xml)', 'metabolic models', 'flux analysis') to broaden trigger-term coverage.

Confirm third-person voice is maintained (it is) and keep the description concise when adding the trigger clause.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions—'FBA, FVA, gene knockouts, flux sampling, SBML models'—with comprehensive coverage of COBRApy's analytical capabilities.	5 / 5
Completeness	Has a clear 'what' but no explicit 'when/Use when...' trigger clause; per the rubric a missing explicit trigger guidance caps completeness at 3.	3 / 5
Trigger Term Quality	Strong domain keywords (FBA, FVA, gene knockouts, flux sampling, SBML) that a systems-biology user would naturally say, but lacks common synonyms/variations and explicit file extensions like '.xml' or 'metabolic model'.	4 / 5
Distinctiveness Conflict Risk	The COBRA/metabolic-modeling niche is highly specific with distinct technical triggers (FBA, FVA, SBML, flux sampling), minimizing overlap with other skills.	5 / 5
	Total	17 / 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
referenced_paths_exist	Referenced path issues: 2 missing	Warning

	Total	14 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/bio/cobrapy/SKILL.md
Commit: df37802

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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