scientific-paper-quality

What problem does it solve?

Quantitatively evaluate scientific manuscript quality to support pre-submission assurance by delivering multi-dimensional metrics across readability, structure, and vocabulary, helping authors improve before submission.

Core Features & Use Cases

• Readability metrics (Flesch-Kincaid, Gunning Fog, average sentence length, etc.) to diagnose readability issues.
• Structural quality assessment (IMRAD balance, section-length distribution, figure/table coverage) to ensure balanced manuscript framing.
• Vocabulary quality analysis (type-token ratio, hedges, redundancy, overclaim detection) to improve precision and clarity.
• Journal compliance checks (word limits, abstract length, figure/references limits, data availability, author contributions and conflicts of interest) for common journals.
• Reproducibility checks (statistical method coverage, software versions, data availability) to strengthen methods reporting.
• Use Case: Before submission, a team can run this to generate a quality_report.json and use it to guide revisions.

Quick Start

Run the quality check pipeline on manuscript/manuscript.md to generate manuscript/quality_report.json.