scholar-code-review
scholar-code-review is a Multi-agent review & consensus skill for AI agents, published by joshzyj in the open-scholar-skill repository on GitHub. Skillspace rates scholar-code-review D (60/100) and it passed the safety scan; the rating is based on static analysis only.
Systematic multi-agent code review of all analysis scripts produced in a project. 6 specialized agents review for: (1) correctness & logic errors, (2) robustness & defensive coding, (3) statistical implementation fidelity, (4) reproducibility & replication readiness, (5) code style & AI-generated anti-patterns, (6) data handling & variable construction (miscoded categories, wrong recodes, mishandled missing values, sample restrictions). Produces a consolidated review report with severity-ranked issues, fix checklist, and a per-script scorecard. Run after /scholar-analyze, /scholar-compute, or /scholar-eda to catch coding errors before manuscript drafting.
- Grade
- D
- Quality
- 60 / 100
- Safety
- Passed
- GitHub stars
- 102
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 74 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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