Content
82%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A lean, actionable skill body with executable commands, real bundle references, and a well-sequenced workflow that includes quality checkpoints and gotchas. Minor gains are available from tightening basic parameter explanations and adding an explicit validation retry loop.
Suggestions
Trim basic kinetic definitions (e.g. 'lower Km = tighter binding', 'apparent affinity') that Claude already knows, keeping only tool-specific interpretation notes.
Add an explicit validate-and-retry feedback loop, e.g. after the fit: 'If residuals show systematic curvature or R²<0.98, do not report Km/Vmax — widen the [S] range or consider substrate inhibition / a Hill model.'
Consider moving the full parameter and inhibition-mode reference tables into a references/ file referenced one level deep, keeping SKILL.md as a tighter overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with task-specific interpretation tables and a pointed Lineweaver-Burk warning that earns its place; minor over-explanation of basic parameter meanings (e.g. 'lower Km = tighter binding') that could be trimmed since Claude already knows MM kinetics. | 4 / 5 |
Actionability | Fully executable copy-paste-ready `tu run EnzymeKinetics_calculate` commands with concrete argument values for both the michaelis_menten and inhibition operations, plus a real referenced script (`scripts/fit_michaelis_menten.py`) that exists in the bundle. | 5 / 5 |
Workflow Clarity | Clear five-step sequence (Prepare → Fit → Interpret → Inhibition → Gotchas) with quality checkpoints (R²≥0.98, residual-curvature check, Km-in-range trustworthiness) and error-recovery cues (flag substrate inhibition rather than forcing a fit); not a full 5 because there is no explicit validate-then-retry feedback loop. | 4 / 5 |
Progressive Disclosure | Well-organized sections with a clearly signaled one-level-deep reference to a real bundle file (`scripts/fit_michaelis_menten.py`) and a related-skills pointer; the detailed parameter and inhibition tables are concise enough to live inline, though a dedicated reference file could carry the fuller interpretation guidance. | 4 / 5 |
Total | 17 / 20 Passed |