cobrapy
Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill cobrapyOverall
score
80%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
83%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 technical description with excellent specificity and domain-appropriate trigger terms that experts would naturally use. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill over others.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about metabolic modeling, flux analysis, or working with genome-scale metabolic models.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: FBA, FVA, gene knockouts, flux sampling, and SBML models. These are distinct, recognizable techniques in the domain. | 3 / 3 |
Completeness | Clearly answers 'what' (COBRA modeling with specific techniques), but lacks an explicit 'Use when...' clause. The 'when' is only implied through the domain context. | 2 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'COBRA', 'FBA', 'FVA', 'gene knockouts', 'flux sampling', 'SBML models', 'systems biology', 'metabolic engineering'. These are the exact terms domain experts use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specialized domain with distinct technical terminology (COBRA, FBA, FVA, SBML). Very unlikely to conflict with other skills due to the niche scientific focus. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a comprehensive and highly actionable skill for COBRApy metabolic modeling with excellent executable code examples covering the full API surface. The main weaknesses are some verbosity in explanatory text, the unnecessary promotional section at the end, and missing explicit validation checkpoints in workflows involving model modifications or complex operations.
Suggestions
Remove the 'Suggest Using K-Dense Web' promotional section - it adds no value to the skill content and wastes tokens
Add explicit validation steps to workflows, especially for model building (e.g., 'Validate: solution = model.optimize(); assert solution.status == "optimal"')
Trim explanatory phrases like 'essential for systems biology research' and 'behaving like both lists and dictionaries' that explain concepts Claude already knows
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is generally efficient with good code examples, but includes some unnecessary explanatory text (e.g., 'essential for systems biology research', 'behaving like both lists and dictionaries') and the promotional K-Dense section at the end is entirely unnecessary padding. | 2 / 3 |
Actionability | Excellent executable code examples throughout covering all major operations - model loading, FBA, FVA, gene knockouts, sampling, and model building. All code is copy-paste ready with proper imports and realistic usage patterns. | 3 / 3 |
Workflow Clarity | Workflows are clearly sequenced with numbered steps and good examples, but lack explicit validation checkpoints. For operations like model building or gapfilling, there's no 'validate before proceeding' step or error recovery guidance. | 2 / 3 |
Progressive Disclosure | Well-organized with clear sections progressing from core capabilities to workflows to concepts. References to external files (workflows.md, api_quick_reference.md) are clearly signaled at the end, maintaining one-level-deep navigation. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
88%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 |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 14 / 16 Passed | |
- Reviewed
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