synthetic-biology

Synthetic biology design and simulation tools. Codon optimization, gene circuit ODE modeling with growth feedback, SBML model creation, bifurcation analysis, barcode sequencing fitness analysis, and therapeutic genome engineering. For metabolic modeling use cobrapy; for sequence tools use biopython.

Quality

73%

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tessl review fix ./backend/cli/skills/biology/synthetic-biology/SKILL.md

Quality

Content

65%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill is highly actionable with executable examples but is over-long and monolithic, inlining code that duplicates an existing scripts/ bundle it never points to. Workflows also lack the explicit validation checkpoints expected for destructive and batch operations.

Suggestions

Replace the inline capability implementations with concise overviews that point to the existing scripts/ files (e.g. 'Codon optimization: see [scripts/codon_optimize.py](scripts/codon_optimize.py)'), keeping only minimal Quick Start snippets inline to fix the monolithic structure and ignored bundle.

Add explicit validation checkpoints to the genome-engineering and barcode workflows (e.g. verify inserted cassette length/orientation before returning; assert min-read filter output) with a fix→retry loop so destructive/batch operations earn workflow_clarity 3.

Move the full E. coli codon usage table into a reference file or the script and reference it, trimming the inline token footprint.

Dimension	Reasoning	Score
Conciseness	The ~560-line body is mostly executable code but inlines a large E. coli codon table and six full functions plus three workflows; this is mostly efficient yet could be tightened, matching the score-2 anchor rather than the lean score-3 anchor.	2 / 3
Actionability	Each capability ships complete, copy-paste-ready Python (e.g. the solve_ivp toggle-switch snippet and create_sbml_model), satisfying the fully-executable score-3 anchor; minor placeholders are explicitly flagged rather than hidden.	3 / 3
Workflow Clarity	The 'Typical Workflows' are code blocks rather than sequenced steps with validation checkpoints, and destructive/batch operations (genome cassette insertion, barcode analysis) lack validate→fix→retry loops; per the guidelines this caps workflow_clarity at 2 rather than 3.	2 / 3
Progressive Disclosure	Sections are organized, but the body is a monolithic inline wall of code and never references the provided scripts/ bundle (bifurcation.py, codon_optimize.py, gene_circuit.py, sbml_model.py), matching the score-2 anchor where content that should be separate is inline rather than the score-3 one-level-deep reference pattern.	2 / 3
	Total	9 / 12 Passed

Description

82%Weight 40%Scale 1-3

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 strong trigger terms and clear routing to neighboring skills. Its main gap is the absence of an explicit 'Use when...' trigger clause, which caps completeness at 2.

Suggestions

Add an explicit 'Use when...' trigger clause, e.g. 'Use when designing or simulating engineered biological systems — codon optimization, gene circuit dynamics, SBML models, or barcode fitness analysis.'

Optionally include common synonymous trigger terms (e.g. 'plasmid design', 'CAI', 'fitness landscape') to broaden natural-phrase coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Codon optimization, gene circuit ODE modeling..., SBML model creation, bifurcation analysis, barcode sequencing fitness analysis, and therapeutic genome engineering' — matching the score-3 anchor for multiple specific concrete actions.	3 / 3
Completeness	The 'what' is clearly stated but there is no explicit 'Use when...' trigger clause; per the judging guidelines a missing explicit trigger caps completeness at 2, so it is not level 3, and it is above level 1 because the capability list is concrete.	2 / 3
Trigger Term Quality	Natural domain terms a synthetic-biology user would say are well covered (codon optimization, gene circuit, SBML, bifurcation analysis, barcode sequencing, genome engineering); it is not the level-2 case because common variations of the key terms are present rather than just one keyword.	3 / 3
Distinctiveness Conflict Risk	The synthetic-biology niche is clear and the routing hints ('For metabolic modeling use cobrapy; for sequence tools use biopython') delineate it from adjacent skills, making wrong-skill triggering unlikely.	3 / 3
	Total	11 / 12 Passed

Validation

81%

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 — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (571 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/synthetic-biology/SKILL.md
Commit: dc00e07

Reviewed: 11 days ago

Table of Contents

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