microbial-dynamics

Microbial population dynamics modeling and analysis. Bacterial growth curve fitting (logistic, Gompertz, Baranyi), Lotka-Volterra community dynamics, Gillespie stochastic simulation, biofilm quantification, CFU enumeration, and genome annotation. For metabolic modeling use cobrapy; for sequence analysis use biopython.

Quality

73%

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tessl review fix ./backend/cli/skills/biology/microbial-dynamics/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.

A thorough, code-rich skill body with highly actionable, executable examples, but it is padded by redundant examples repeated across Quick Start, capability sections, and workflows, and it ignores the provided bundle scripts entirely. Adding validation checkpoints and pointing to the bundle files would meaningfully improve it.

Suggestions

Replace the duplicate Quick Start and Workflow examples with one-line references to the canonical section, or move canonical implementations into scripts/ and link to them, removing the redundancy.

Add explicit validation checkpoints to batch/risky workflows (e.g. check curve_fit convergence and R² before reporting parameters; verify the Prokka .gff exists and is non-empty before parsing it).

Link the existing scripts/ files (growth_curve.py, lotka_volterra.py, biofilm_assay.py, cfu_calculator.py, colony_counter.py) from the relevant sections so the bundle is actually used rather than reimplemented inline.

Dimension	Reasoning	Score
Conciseness	Mostly efficient code-heavy content, but redundant: the Quick Start re-states the logistic model later repeated in section 1, Workflow 2 duplicates the Lotka-Volterra example from section 2, and the Overview re-summarizes capabilities already in the description. Not a 1 because it avoids explaining concepts Claude already knows; not a 3 because of the repetition that could be tightened.	2 / 3
Actionability	Provides fully executable, copy-paste-ready Python throughout — curve_fit, solve_ivp, a complete Gillespie SSA, Prokka subprocess invocation, and CFU calculation — with realistic example data and concrete commands.	3 / 3
Workflow Clarity	Capabilities and workflows are sequenced, but batch/risky operations lack inline validation checkpoints: genome annotation runs Prokka then parses the GFF without validating intermediate output, the growth-curve workflow never verifies that fits converged, and CFU enumeration has no checkpoint. Per the guidelines, missing validation/feedback loops in batch operations caps workflow clarity at 2.	2 / 3
Progressive Disclosure	The body is organized into clear sections but is monolithic inline code; the five provided bundle scripts (biofilm_assay.py, cfu_calculator.py, colony_counter.py, growth_curve.py, lotka_volterra.py) are never referenced or linked, so content that should be split or signaled to the bundle is instead duplicated inline.	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 strong, specific description that names concrete capabilities and distinguishes itself from neighboring skills, but it never states when Claude should invoke it, which caps completeness. Adding an explicit 'Use when...' trigger clause would raise it to a top score.

Suggestions

Add an explicit 'Use when...' clause listing natural triggers, e.g. 'Use when fitting bacterial growth curves, modeling multi-species communities, running Gillespie simulations, quantifying biofilms, enumerating CFUs, or annotating genomes.'

Include a few more colloquial trigger phrases users might say (e.g. 'count my colonies', 'fit my OD600 data') alongside the technical terms to broaden trigger coverage.

Dimension	Reasoning	Score
Specificity	Enumerates multiple concrete actions such as 'Bacterial growth curve fitting (logistic, Gompertz, Baranyi)', 'Lotka-Volterra community dynamics', 'Gillespie stochastic simulation', 'biofilm quantification', 'CFU enumeration', and 'genome annotation', matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	It comprehensively answers 'what does this do' but lacks any explicit 'Use when...' or equivalent trigger clause; per the guidelines a missing explicit trigger guidance caps completeness at 2.	2 / 3
Trigger Term Quality	Includes natural domain terms a microbiology user would actually say — 'growth curve fitting', 'biofilm quantification', 'CFU enumeration', 'genome annotation' — giving good coverage of natural trigger language, though slightly technical rather than colloquial.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear microbial-dynamics niche and explicitly disambiguates adjacent skills ('For metabolic modeling use cobrapy; for sequence analysis use biopython'), making conflict with other skills 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 (537 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/microbial-dynamics/SKILL.md
Commit: dc00e07

Reviewed: 11 days ago

Table of Contents

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