The body is lean and operationally focused — compact tables and code blocks with no padded prose or generic concept explanations; the interpretive notes are tied to the tool's output (e.g. 'R²≥0.98 good; check residuals') rather than filler.

Provides fully executable, copy-paste-ready `tu run EnzymeKinetics_calculate '{...}'` commands with real data arrays and a runnable script path, plus concrete parameter interpretation guidance.

Clear Step 1–5 sequence with explicit validation checkpoints (Km must lie inside the tested range, R²≥0.98, residual-curvature checks, substrate-inhibition flag) and error-recovery guidance in the Gotchas section.

Well-organized sections with a single one-level-deep reference to the real bundle file `scripts/fit_michaelis_menten.py` and a Related skills pointer; the SKILL.md stays an overview while the executable detail lives in the script.

Lists concrete actions ('Km, Vmax, kcat... kcat/Km', 'inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki)', 'Fits the MM equation by nonlinear regression'), but the second-person phrasing 'Use when you have substrate concentrations' incurs the rubric's −1 specificity penalty.

Explicitly answers both what ('Fits the MM equation by nonlinear regression... inhibition-mechanism analysis') and when ('Use when you have substrate concentrations and initial reaction velocities and need kinetic parameters or to classify an inhibitor').

Natural user-facing terms are well covered — 'Michaelis-Menten', 'Km', 'Vmax', 'kcat', 'substrate-velocity data', 'classify an inhibitor', 'kinetic parameters' — matching what a user would actually say.

Clear niche with an explicit negative trigger ('NOT for BRENDA database lookups of published constants (use the BRENDA tools)') that disambiguates it from sibling skills.