Domain-correctness gate

Across every field the top complaint is code that executes cleanly but is scientifically wrong. This gate intercepts that field's classic error classes deterministically — by analysing the code you actually wrote, not by recalling rules. It verifies specific error classes; it never proves correctness.

It is fast, offline and stdlib-only — but it is an on-request gate, not a background one.

When to run

• When the user asks to check, validate, review or audit an analysis for correctness.
• When you are the reviewer agent, running the checks that agent's prompt lists (the app's auto-review turn, which the user switches on in Settings).

Do NOT run it on your own initiative while writing or executing analysis code. A clean run still emits a review block, so an unprompted pass puts a card with no findings in the user's conversation — noise they did not ask for.

How to run

The gate ships beside this SKILL.md. Run it on the code files in play (or with no arguments to scan the workspace):

python "$XDG_CONFIG_HOME/opencode/skills/domain-check/domain_check.py" <file.py|notebook.ipynb|analysis.R ...>

It prints exactly one ```review fenced JSON block on stdout.

What it catches (one rule set per discipline)

• physics · units — adding/subtracting/comparing quantities of different dimensions (e.g. t_seconds + d_meters); trig on a degree-valued angle.
• earth · crs — Euclidean/Pythagorean distance on latitude/longitude (sqrt((lat1-lat2)**2 + (lon1-lon2)**2)); a geopandas geometric op with no CRS ever set.
• biology · coords / strand — off-by-one on BED intervals (0-based half-open, so length is end - start, never +1); a sequence sliced from a stranded feature file (GFF/GTF/BED) with no reverse-complement for the - strand.
• chem · valence — a SMILES string literal (assigned to a smiles/smi variable, or passed to MolFromSmiles/MolFromSmarts) that cannot be a real molecule. If RDKit is installed it is used as the authoritative judge — Chem.MolFromSmiles sanitizes the parse, so it catches far more than a five-bond carbon (bad ring closures, impossible aromaticity, over-valent N/O/S) and, being authoritative, clears molecules a heuristic would wrongly flag. Without RDKit it falls back to a stdlib bond-counter (carbon

  4, over-bonded halogen; bails on bracket atoms for precision).

• social · multiple-comparisons — a significance test (ttest_ind, pearsonr, f_oneway, chi2_contingency, …) run inside a loop or ≥3 times with no multipletests/FDR/Bonferroni correction anywhere — the inflated family-wise false-positive rate that silent p-hacking produces.
• social · categorical — a numeric reduction (.mean()/.median()/.std() …) taken directly on a nominal category code (gender, race, region, condition, …), treating an unordered label as an interval quantity. A groupby('gender') key is correct usage and is not flagged.
• bioprocess · unconstrained-kinetics — curve_fit fitting a local model function whose parameters are classic non-negative kinetic constants (Monod/Haldane mu_max/Ks/Ki, Pirt/yield Yxs/Yps, Luedeking-Piret alpha/beta, …) with no bounds=, letting a noisy or sparse fit converge to a physically impossible negative value. Names that belong to any curve at all (alpha, beta, kd, ka) count only beside an unambiguous one or in a file that otherwise reads as a fermentation, so a plain power law, whose exponent is routinely negative, is left alone.
• bioprocess · kla-driving-force — in a file that computes kLa, taking log() of the raw dissolved-oxygen reading instead of the (C* - C) driving force the dynamic gassing-out method requires (dC/dt = kLa(C*-C), so the regression is on ln(C* - C), not ln(C)).
• bioprocess · cfu-log-scale — a numeric reduction (.mean()/.std()/…) taken directly on a raw CFU (colony-forming-unit) plate count. Microbial counts are approximately log-normal; the convention is to average log10(CFU), not the raw count. A variable already named as the log quantity (log_cfu) or a .mean() taken after np.log10(...) is not flagged.
• bioprocess · anova-assumptions — f_oneway/anova_lm/ pairwise_tukeyhsd/tukey_hsd run in a file with no normality (shapiro/normaltest/anderson) or variance-homogeneity (levene/bartlett/fligner) check anywhere in it — the two assumptions the test's stated false-positive rate depends on. Scoped to files that read as a fermentation (biomass, bioreactor, CFU, fed-batch, …): the statistics generalize, but a finding tagged bioprocess on a three-arm survey does not.
• bioprocess · rsm-first-order-fit — a Box-Behnken/central-composite design (bbdesign/ccdesign/box_behnken/central_composite in the file) fit through a statsmodels formula with no quadratic (I(x**2)) or interaction (x1:x2) term — the design was built to estimate curvature, so a first-order model wastes it and cannot locate an interior optimum.
• bioprocess · cross-scale-fit — a real model fit (.fit(...), guarded to a file that imports scikit-learn, XGBoost, statsmodels, PyTorch, TensorFlow, or Keras — a plain scipy.curve_fit never counts) in a file that mentions both a small-scale (flask, bench-scale) and a large-scale (bioreactor, fermenter, fed-batch, pilot-scale) process vocabulary, e.g. trained on flask data and applied to a bioreactor. Advisory: a warn, not a defect. Silent once a calibration / scaling-factor / cross-validation term is in the file, and silent on batch_size and friends — bare batch matched the training knobs of the very libraries this rule requires.

Rules favour precision: an unrecognized unit, arithmetic with no discipline signal, a SMILES using bracket atoms (which carry their own valence/charge), a single significance test, or a categorical used only as a groupby key is left silent rather than flagged.

Reporting findings

Copy the ```review block the tool prints as the last thing in your message — the app renders it as dismissible reviewer cards. Do not paraphrase the findings into prose and drop the block; the structured block is the contract. If the gate found nothing, say so plainly and keep the block (its note states that no findings is not a guarantee of correctness).

Never tell the user the code is "correct" or "error-free" — the gate checks known error classes only.

Adding a discipline

Add a check_<field>(ctx) function in domain_check.py and append it to VALIDATORS. No other change is needed — the review contract and the app's rendering are discipline-agnostic (each finding carries its own tag).