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):

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.

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).