# Domain Check > Use whenever you write or run scientific analysis code (physics, earth/geo, biology, chemistry, social science, or bioprocess/fermentation) in this workspace — before executing it and again after generating results. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct. - Skill: `ai4s-research/domain-check` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add ai4s-research/domain-check` - Raw SKILL.md: https://api.skillmd.com/api/skills/ai4s-research/domain-check/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: ai4s-research (https://skillmd.com/u/ai4s-research) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ai4s-research/domain-check --- # 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. Run it as a normal step of any analysis — it is fast, offline, and stdlib-only. ## When to run - **Before executing** analysis code you generated (catch the bug before it produces a plausible-but-wrong number). - **After generating results**, as a final gate before you report figures or numbers to the user. - Whenever the user asks to check, validate, or audit an analysis for correctness. ## 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): ```bash python "$XDG_CONFIG_HOME/opencode/skills/domain-check/domain_check.py" ``` 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_(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`).