Academic Paper Dual-Track Review and Flaw Scanning

Problem to Solve

Academic rejections are rarely caused by formatting errors alone. A manuscript may need layered checks for theoretical contribution, literature-gap validity, research-design transparency, argument consistency, and data or citation coherence. This skill separates objective scanning from substantive review, then merges the results into an actionable revision list.

How It Works

• Track 1 · local mechanical scan: a local rule engine evaluates seven dimensions, including layout, content consistency, feasibility, validity, AI-style traces, academic norms, and journal fit. It does not upload the content and produces report_summary.md, scores.json, and report_detail.json.
• Track 2 · expert consultation: roles can be invoked by issue, such as theory contribution, methods, literature dialogue, logic chain, and editor desk screening. Each critique must point to specific paragraphs and identify fatal gaps, fixes, or an initial screening score.

In full review mode, it starts with editor screening, then checks theory, literature, methods, and logic, finishes with the mechanical scan, and returns a combined score, prioritized fixes, and the three most urgent issues.

Boundaries

It does not replace plagiarism checking, grammar polishing, or manuscript rewriting. AI-trace detection is heuristic and may flag careful human writing. Non-academic texts are outside the intended scope, and conclusions still require human review.