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A single pre-deployment command can catch many mechanical defects in a bilingual math-problem release: malformed records, answer mismatches, likely duplicates, translation drift, invalid generated SQL, and sitemap-link errors. In a project case study published September 26, 2026, MozgoQuest contributor Ivan Nedomolkov describes using reviewed YAML as the source of truth and running npm run content:check before deployment. The reported run covered 180 original problems in Russian and English. It is an account of one project’s pipeline, not an independent audit; automated checks also cannot judge whether a problem or translation is good teaching.
What the pipeline checks
Nedomolkov’s account describes MozgoQuest, a free math-practice project for grades 1–6. Its editable problem records live in reviewed YAML; SQL migrations and a JavaScript translation bundle are generated from those files. The content check runs a sequence of validations against the authored data and generated outputs. The author says AI helped draft and edit content, but he checked claims and commands against the repository and reran the pipeline. Read the DEV Community article.
The stages are designed to stop different classes of release errors. Passing them means the content meets the checks the project has defined; it does not establish that every problem is correct in a broader mathematical or pedagogical sense.
1. Record structure and editorial rules
Schema validation runs before checks that depend on complete fields. The validator checks required metadata and content conventions, including:
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- Unique slugs and IDs, plus valid slug formats.
- Grade and difficulty ranges and approved topic vocabulary.
- Statement and explanation lengths, numeric answers, and authorship metadata.
- Two distinct, substantial hints for each problem.
- Forbidden competition names.
This catches incomplete or inconsistent records early, before downstream generation could turn them into broken or misleading public content.
2. Similarity guardrails for duplicate questions
The validator normalizes case and punctuation before comparing statements. The project’s reported failure thresholds are 0.86 similarity among authored statements and 0.70 against recovered legacy material. These are project-specific guardrails, not universal originality standards: a score can flag text for review, but it cannot prove that a problem is original or establish ownership. The author says metadata and editorial review remain part of that judgment.
3. Numeric answer verification
Each problem has an expected answer and a separate verification expression. Rather than pass expressions to unrestricted eval, the evaluator parses a restricted Python abstract syntax tree and permits only sum, range, gcd, and lcm as callable names. The project applies its own numeric tolerance rules; if the computed result and expected answer disagree under those rules, the check stops the build.
This can expose a wrong stored answer or a mismatch between an answer and its verification expression. It does not independently validate the assumptions or wording of the underlying word problem.
4. Russian–English parity and review status
Russian and English records must use the same slugs and form an exact one-to-one set. The validator compares grade, topic, answer, two-hint structure, and numbers appearing in statements, explanations, and hints. Each translation also needs an explicit review status. Missing or unreviewed translations are left out of the public runtime bundle.
Number checks can catch a translation that changes a quantity, but they cannot determine whether English is natural, clear, or equivalent in meaning. That still needs a person who can review the language.
5. Generated SQL and public sitemaps
The pipeline applies generated SQL to an in-memory SQLite database and checks problem and hint counts, intended IDs, and inactive status. The release flow described by the author inserts rows and hints as inactive, checks the resulting rows and status, and activates only the intended ID range. This separates generating and checking a release from exposing it publicly.
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The content check also rebuilds sitemaps for both languages and verifies reciprocal hreflang links. In the run reported in the article, the Russian sitemap had 230 URLs and the English sitemap 231; each had 180 task pages and 16 populated grade-topic hubs. These are snapshot counts from that project’s run, not general expectations for a math site.
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How to run the reported content check
- From the project repository, run
npm run content:check. - Review any failure before deployment. A schema error should be fixed in the authored YAML first; do not treat generated SQL or translation output as the source of truth.
- Rerun the command after corrections and confirm the output covers the intended content sets, answer checks, generated problems and hints, sitemaps, and reciprocal language links.
- Proceed to the project’s broader release checks. The author says unit tests, browser scenarios, a Worker dry run, and public health checks sit outside this content-specific command.
The published output reported four YAML sets validated, 180 original questions with 30 per grade across grades 1–6, 180 numeric answers verified, 180 self-reviewed English translations compiled, 180 problems and 360 hints built, and both sitemaps generated with reciprocal hreflang validated. These are the author’s reported results, not an independent reproduction.
What automation cannot decide
Mechanical validity is not the same as instructional quality. As Nedomolkov puts it, “Automation can prove that two stored numbers match. It cannot prove that a problem is interesting, age-appropriate, clearly worded, or pedagogically useful.” Human review should ask whether a child can understand the task without hidden context, whether the first hint still leaves room to solve it, whether the second hint teaches a method without simply giving away the answer, whether the explanation conveys a reusable idea, and whether the English reads naturally.
The practical boundary is clear: let checks enforce repeatable contracts and block known classes of defects; keep people responsible for mathematical sense, language, originality decisions, and teaching value.
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