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tooluniverse-enzyme-kinetics

Enzyme kinetics — Michaelis-Menten Km, Vmax, kcat (turnover), and kcat/Km (catalytic efficiency / specificity constant) from substrate-velocity data, plus inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki). Fits the MM equation by nonlinear regression (and reports Lineweaver-Burk for reference). Use when you have substrate concentrations and initial reaction velocities and need kinetic parameters or to classify an inhibitor. NOT for BRENDA database lookups of published constants (use the BRENDA tools).

72

Quality

87%

Does it follow best practices?

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SecuritybySnyk

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The canonical home for this skill is tooluniverse-enzyme-kinetics in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 a strong, actionable overview with executable commands, interpretation tables, and validation guidance. Its main shortfalls are the placeholder inhibition example, gotchas-as-recovery rather than an explicit retry loop, and inline (rather than consolidated) references.

Suggestions

Replace the `[...]` placeholders in the Step 4 inhibition `tu run` example with a concrete substrate/velocity payload so it is copy-paste ready like the Step 2 example.

Promote the validation checks into an explicit feedback loop (e.g., 'if R2<0.98 or residuals show curvature, do not report values — widen [S] or consider substrate inhibition and re-fit').

Add a short 'References / bundle files' section that consolidates the `scripts/fit_michaelis_menten.py` pointer and any deeper tool-output field semantics in one clearly signaled place.

DimensionReasoningScore

Conciseness

Dense and domain-specific with tables and executable commands rather than textbook padding; a few parameter-interpretation asides ('lower Km = tighter binding') are mild over-explanation a competent model already knows, keeping it just short of fully lean.

4 / 5

Actionability

Provides a copy-paste-ready `tu run` command with a full JSON payload for the primary MM fit and a documented CSV script, but the inhibition example uses `[...]` placeholders rather than a fully concrete payload.

4 / 5

Workflow Clarity

A clear 5-step sequence with validation checkpoints (R2>=0.98, residual-curvature checks, Km-outside-range warning, substrate-inhibition flag) and a self-validating script, though recovery is framed as gotchas rather than an explicit validate->fix->retry loop.

4 / 5

Progressive Disclosure

Well-organized overview with clear section headers and a single one-level-deep bundle reference (`scripts/fit_michaelis_menten.py`) signaled inline; references are not consolidated into a dedicated section and some tool-output detail lives inline, leaving minor organization gaps.

4 / 5

Total

16

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20

Passed

Description

100%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 exemplary: it is specific, trigger-rich, explicitly answers both what and when, and draws a sharp boundary against the related BRENDA and dose-response skills. No meaningful gaps to address.

DimensionReasoningScore

Specificity

Lists multiple concrete outputs (Km, Vmax, kcat, kcat/Km, inhibition classification, Ki) plus concrete methods (nonlinear regression, Lineweaver-Burk), giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both 'what' (computes kinetic parameters and inhibition mechanisms) and 'when' ('Use when you have substrate concentrations and initial reaction velocities...'), reinforced by an explicit NOT-for boundary clause.

5 / 5

Trigger Term Quality

Includes natural user phrases like 'substrate concentrations and initial reaction velocities', 'kinetic parameters', and 'classify an inhibitor' with synonyms covering the common ways a user would request this.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (fitting enzyme kinetics from the user's own measured data) with explicit disambiguation against the BRENDA lookup tools and a related dose-response sibling, minimizing conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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