cobrapy

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

Quality

73%

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 ./backend/cli/skills/biology/cobrapy/SKILL.md

Quality

Content

76%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 highly actionable with executable code throughout and a clean progressive-disclosure structure backed by real reference files. The main weakness is workflow clarity: batch/destructive operations lack explicit validation checkpoints, which caps that dimension.

Suggestions

Add explicit validation checkpoints to the batch workflows (e.g. in Workflow 2, verify solution status and sanity-check the deletion results before classifying essential genes).

De-duplicate the Common Workflows section against references/workflows.md, keeping only quick examples inline and pointing to the reference for full sequences.

Tighten the Overview paragraph to remove "essential for systems biology research" framing that Claude does not need.

Dimension	Reasoning	Score
Conciseness	Largely lean code-driven content with minimal padding, but the Common Workflows section repeats material already shown in Core Capabilities (e.g. knockouts, FVA) and could be trimmed.	4 / 5
Actionability	Abundant copy-paste-ready, executable code covering load/optimize/FVA/knockouts/sampling/building; specific examples address the common cases comprehensively.	5 / 5
Workflow Clarity	Workflows are clearly sequenced, but batch operations like the gene-knockout screen (Workflow 2) and double deletions lack explicit validation checkpoints/feedback loops, capping workflow clarity at 3 per the rubric.	3 / 5
Progressive Disclosure	Good structure with a clearly signaled References section pointing one level deep to real bundle files (workflows.md, api_quick_reference.md); minor gap is that Common Workflows duplicates content already in references/workflows.md.	4 / 5
	Total	16 / 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 well-targeted to a distinct domain, but it lacks an explicit "Use when..." trigger clause, leaving the "when" only weakly implied and capping completeness. Trigger term coverage is strong but could add common synonyms and file extensions.

Suggestions

Add an explicit trigger clause, e.g. "Use when analyzing genome-scale metabolic models, running FBA/FVA, or performing gene knockout studies."

Include natural synonyms and file extensions users might say ("flux balance analysis", ".sbml", ".xml", "metabolic model") to broaden trigger term coverage.

Optionally note one or two more capabilities (model building, gapfilling) to round out coverage.

Dimension	Reasoning	Score
Specificity	Lists several concrete capabilities ("FBA, FVA, gene knockouts, flux sampling, SBML models") with minor coverage gaps (no model building, gapfilling, media design).	4 / 5
Completeness	Clear "what" is present, but there is no explicit "Use when..." trigger clause; the "when" is only weakly implied via "for systems biology and metabolic engineering analysis", capping completeness at 3 per the rubric guideline.	3 / 5
Trigger Term Quality	Strong domain keywords a user would say (FBA, FVA, gene knockouts, flux sampling, SBML, metabolic engineering) but misses spelled-out synonyms like "flux balance analysis" and file extensions like .xml/.sbml.	4 / 5
Distinctiveness Conflict Risk	A clear niche (constraint-based metabolic modeling / COBRA) with distinct technical triggers (FBA, FVA, SBML, gene knockouts) and minimal overlap risk with unrelated skills.	5 / 5
	Total	16 / 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/cobrapy/SKILL.md
Commit: 3a6c3a9

Reviewed: 1 day 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.