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cobrapy

Constraint-based reconstruction and analysis (COBRA) for metabolic models; use when you need to simulate growth/production, analyze flux ranges, or run knockout and medium studies from SBML/JSON/YAML models.

62

Quality

75%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./scientific-skills/Data Analysis/cobrapy/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%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 skill delivers solid, executable COBRApy guidance and a runnable example, but nearly half the body is duplicated generic boilerplate and the reference files are never actually named or linked. Tightening the padding and surfacing the references would markedly improve it.

Suggestions

Remove the five duplicated generic sections (When to Use, Key Features, Dependencies, Example Usage, Implementation Details) that restate the real sections as boilerplate, keeping only the COBRApy-specific versions.

Replace generic 'references/' mentions with explicit links to references/api_quick_reference.md and references/workflows.md, and move the inlined API signatures into the quick reference file.

Add a concrete validation checkpoint (e.g., a command or assertion that the FBA solution status is 'optimal' before interpreting fluxes) to the Recommended Workflow instead of the abstract 'run a final validation pass' step.

DimensionReasoningScore

Conciseness

The body duplicates five sections (When to Use, Key Features, Dependencies, Example Usage, Implementation Details) as generic boilerplate alongside their real versions, and adds several padded process sections (Output Contract, Validation Rules, Completion Checklist, Quick Validation) — noticeably verbose with several unnecessary padded blocks.

2 / 5

Actionability

Provides a complete, runnable Python example covering FBA, pFBA, FVA, gene deletion, medium editing, and sampling, plus concrete API calls in Implementation Details; minor gaps are solver-installation setup and abstract workflow steps.

4 / 5

Workflow Clarity

A 4-step Recommended Workflow and a numbered code sequence exist, but validation checkpoints are abstract ('run a final validation pass') with no concrete verify commands, and Quick Validation states no local validation step is required — checkpoints are implicit for batch model operations.

3 / 5

Progressive Disclosure

Bundle files exist (references/api_quick_reference.md, references/workflows.md) but the body only mentions 'references/' generically without naming or linking them, and API/signature content that belongs in the quick reference is inlined in Implementation Details.

3 / 5

Total

12

/

20

Passed

Description

100%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 concise yet complete: it states the domain, enumerates concrete actions, and provides an explicit 'use when' trigger with file-format synonyms. It is a strong, low-conflict trigger description.

DimensionReasoningScore

Specificity

Names the domain (COBRA for metabolic models) and lists multiple concrete actions — 'simulate growth/production, analyze flux ranges, run knockout and medium studies' — plus supported formats (SBML/JSON/YAML), giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers 'what' (constraint-based reconstruction and analysis for metabolic models) and 'when' via a concrete 'use when you need to...' clause with specific trigger phrases, satisfying the top anchor.

5 / 5

Trigger Term Quality

Includes natural domain phrases users would say ('simulate growth/production', 'flux ranges', 'knockout and medium studies') together with synonym file extensions (SBML/JSON/YAML), matching the comprehensive-coverage anchor.

5 / 5

Distinctiveness Conflict Risk

The COBRA/metabolic-modeling niche with its specialized triggers is clearly distinct from other skills and unlikely to fire for the wrong skill.

5 / 5

Total

20

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

14

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

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