paper-autoraters
paper-autoraters is an LLM evaluation & observability skill for AI agents, published by Ar9av in the PaperOrchestra repository on GitHub. Skillspace rates paper-autoraters B (81/100) and it passed the safety scan; the rating is based on static analysis only.
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 612
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 3 other repositories, in 1 collections
- Scope
- Portable: works in any project. General LLM-as-judge paper scoring rubrics from a published paper
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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