cobrapy

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

Quality

76%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

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

Quality

Content

82%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.

The body is a well-structured, highly actionable reference with executable code across all core COBRA capabilities and clear workflow sequencing. Minor gaps are slight over-explanation in the Overview, inline/bundled workflow duplication, and validation checkpoints being centralized in Best Practices rather than inline in batch workflows.

Suggestions

Trim the Overview paragraph (it restates the frontmatter description) and fold the inline 'Common Workflows' into references/workflows.md to remove duplication with the bundled reference file.

Add explicit inline validation checkpoints in batch workflows (e.g., a 'Verify status and compare to baseline' step in the gene-knockout screen) rather than relying on the Best Practices list.

Insert a one-line 'validate samples before interpreting' checkpoint directly in Workflow 4 instead of only mentioning it in section 8 and Best Practices.

Dimension	Reasoning	Score
Conciseness	Efficient and dominated by tight, executable code blocks, but the Overview paragraph restates the frontmatter description and a few prose comments could be trimmed.	4 / 5
Actionability	Fully executable, copy-paste-ready code covering the common cases (install, load_model, optimize, FVA, deletions, sampling) with specific function signatures and parameters.	5 / 5
Workflow Clarity	Five clearly sequenced workflows with validation present (baseline comparison in Workflow 2, status checks and sample validation noted), but explicit inline validate-checkpoint steps are partially deferred to the Best Practices list.	4 / 5
Progressive Disclosure	Clear overview pointing to two real one-level-deep reference files (api_quick_reference.md, workflows.md) with well-signaled links, but the inline 'Common Workflows' section overlaps with the bundled references/workflows.md, a minor organization gap.	4 / 5
	Total	17 / 20 Passed

Description

70%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.

The description is specific and distinctive for a well-defined niche, with multiple concrete actions and domain-natural trigger terms. Its main weakness is the absence of an explicit 'Use when...' trigger clause, leaving the 'when to use' guidance only weakly implied.

Suggestions

Add an explicit 'Use when...' trigger clause (e.g., 'Use when running FBA/FVA, gene knockout screens, flux sampling, or analyzing SBML metabolic models') to convert the weak 'for...' context into explicit trigger guidance.

Spell out abbreviations and add synonyms/file extensions ('flux balance analysis (FBA)', '.sbml/.xml', 'metabolic model') so trigger terms match natural user phrasing.

Turn 'SBML models' into an action verb phrase (e.g., 'load and export SBML models') for more concrete action coverage.

Dimension	Reasoning	Score
Specificity	Lists several concrete domain actions ('FBA, FVA, gene knockouts, flux sampling') but 'SBML models' is a noun rather than an action verb phrase, leaving minor coverage gaps.	4 / 5
Completeness	Clear 'what' (the listed analyses) but only a weak 'when' via the trailing 'for systems biology and metabolic engineering analysis' clause — no explicit 'Use when...' trigger guidance, so capped at 3 per the judging guidelines.	3 / 5
Trigger Term Quality	Good keyword coverage with domain-natural terms ('FBA', 'FVA', 'gene knockouts', 'SBML', 'systems biology'), but missing spelled-out synonyms ('flux balance analysis'), 'metabolic model', and file extensions ('.sbml', '.xml').	4 / 5
Distinctiveness Conflict Risk	A clear niche (COBRA constraint-based metabolic modeling) with highly distinct trigger vocabulary and minimal overlap risk with other skills.	5 / 5
	Total	16 / 20 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: 49c6e97

Reviewed: 9 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.