cobrapy

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

Quality

71%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./skills/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 with comprehensive executable code, but held back by verbose concept re-explanations and missing validation feedback loops in destructive/batch workflows; progressive disclosure is good but could push the inlined API detail into references.

Suggestions

Trim the Key Concepts section (DictList, exchange reactions, GPR basics) to the non-obvious essentials Claude would not already know, to improve token efficiency.

Add explicit validate→fix→retry checkpoints to the gapfill and model-writing workflows (e.g., run slim_optimize() after gapfill and re-attempt if infeasible).

Move the bulk of the per-capability API snippets into references/api_quick_reference.md and link to them contextually from each section instead of inlining everything in SKILL.md.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and code-driven, but the ~485-line body includes concept sections (DictList objects, exchange reactions, GPR basics) that largely restate things Claude already knows, and could be trimmed.	3 / 5
Actionability	Provides fully executable, copy-paste-ready code across all major capabilities (model loading, FBA, FVA, knockouts, sampling, gapfilling, model building) with specific examples covering the common cases.	5 / 5
Workflow Clarity	Five labeled workflows give a clear sequence, but operations that mutate or remove reactions (gapfill, manual knockout, model writes) lack explicit validate→fix→retry feedback loops; the destructive/batch feedback-loop guideline caps this at 3.	3 / 5
Progressive Disclosure	Good overview structure with bulk detail correctly offloaded to two real one-level-deep references (workflows.md, api_quick_reference.md); however, large API-reference content is inlined in SKILL.md and the references are signaled only at the bottom rather than contextually per section.	4 / 5
	Total	15 / 20 Passed

Description

75%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 specific, well-targeted description with concrete capability terms and a distinct niche, weakened primarily by the absence of an explicit "Use when..." trigger clause that would push completeness higher.

Suggestions

Add an explicit trigger clause, e.g. "Use when loading or analyzing genome-scale metabolic models, running FBA/FVA, performing gene knockouts, or working with SBML models."

Include common synonyms/file extensions users might say, such as ".sbml", ".xml", "metabolic flux analysis", and "genome-scale models" to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — "FBA, FVA, gene knockouts, flux sampling, SBML models" — giving comprehensive coverage of the skill's capabilities rather than vague abstractions.	5 / 5
Completeness	Has a clear "what" ("Constraint-based metabolic modeling (COBRA)" with concrete techniques) but no explicit "Use when..." clause; the trailing "for systems biology and metabolic engineering analysis" only weakly implies when.	3 / 5
Trigger Term Quality	Strong domain keywords (FBA, FVA, gene knockouts, flux sampling, SBML, metabolic engineering) that users would naturally say, but missing synonyms and file extensions (.sbml, .xml) for full coverage.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (COBRA constraint-based metabolic modeling) with distinct technical triggers and minimal overlap risk with other skills.	5 / 5
	Total	17 / 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: b2a92ba

Reviewed: 3 days ago

Table of Contents

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