willbooster/exercode-problem-utils150 installs
review-learning-content
Run only when explicitly invoked to review Exercode learning content for correctness, solvability, pedagogical quality, and writing errors.
How do I install this agent skill?
npx skills add https://github.com/willbooster/exercode-problem-utils --skill review-learning-contentIs this agent skill safe to install?
- Gen Agent Trust Hubpass
The skill reviews learning content and includes instructions to execute code from the repository, such as model answers and judge scripts. While using project-specific tools, the execution of arbitrary code from the content being reviewed presents a low security risk due to potential indirect prompt injection or malicious code within the materials.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
Review Exercode learning content
- Read the applicable authoring skill and its linked references before reviewing:
- generate-course-materials for courses, lecture materials, and embedded questions.
- generate-judge-problems for Judge v2 problems.
- generate-judge-contest for contests and their problem sets.
- Check the learning content for:
- factual accuracy, using authoritative primary sources for material claims that are uncertain or important;
- consistency with the request, surrounding materials, stated prerequisites, and target difficulty;
- clear, unambiguous Japanese, correct terminology, spelling, Markdown, examples, and expected results;
- questions that have exactly the intended answer and can be answered from the question as written;
- code examples that are complete, runnable, and consistent with their explanations;
- courses whose learning objectives include writing, modifying, or executing code provide course-scoped Judge problems at meaningful checkpoints, rather than relying only on embedded questions or complete examples;
- every runtime and third-party dependency required by Judge problem files is declared reproducibly in the repository.
- For each embedded question, independently answer the question from its prompt and options before reading
answerIndex,answerIndices,answerPattern,modelAnswer,hint, orexplanation. Then compare your answer with those fields and verify that the intended answer is unique, correct, and supported by the question as written. - For each Judge problem, first read only its statement and prerequisite materials. Before reading model answers or expected outputs, independently derive a solution, expected example outputs, and important boundary cases. Then inspect the model answers, test cases, templates, and judge implementation and verify that:
- the statement provides enough information to solve the problem uniquely;
- constraints, examples, model answers, expected outputs, and judge behavior agree;
- the intended solution uses only concepts available to the learner;
- test cases cover meaningful boundaries and every model answer is accepted by the judge — run
bunx exercode-problem judge model_answers/<languageId>from the problem directory (it applies the problem's customjudge.tswhen present and the standard stdio judge otherwise; see generate-judge-problems/references/problem-format.md) and confirm the command exits 0, prints at least oneTEST_CASE_RESULTline, and every printed line has the accepted decision code2000; - starter templates do not pass every test case.
- Run the applicable course, problem, and contest validators as supporting evidence. Execute model answers, templates, and judges through the repository-declared runtimes and dependencies. Treat a Judge problem that succeeds only through a global installation, ad hoc download, or temporary dependency environment as a concern. Do not treat validator success as proof of factual, pedagogical, or writing quality.
- Verify every suspected concern before reporting it. Report only concerns with a concrete correctness or learning-quality impact, not preferences.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/willbooster/exercode-problem-utils/review-learning-content">View review-learning-content on skillZs</a>