What problem does it solve?
This Skill automates rigorous, reproducible scoring of research paper drafts so authors and evaluation pipelines can quantify literature-review quality, citation fidelity, and holistic paper quality without manual rubric application.
Core Features & Use Cases
- Citation F1 Partitioning: Partition references into P0/P1 priority buckets and deterministically compute Precision/Recall/F1 against ground-truth via Semantic Scholar ID resolution.
- Literature Review Quality (6-axis): Produce conservative, evidence-based 6-axis JSON scores with penalties and an overall score for Intro + Related Work.
- Side-by-Side (SxS) Comparisons: Run dual-order SxS judgments for full-paper and literature-review-only comparisons to mitigate positional bias.
- Use Case: Validate a paper-generating pipeline by scoring generated drafts against a ground-truth paper, compare two pipeline outputs, or audit literature-review improvements across iterations.
Quick Start
Run the autoraters to score the draft 'submission_v1.pdf' against the ground-truth 'paper_gt.pdf' and produce JSON reports.