Paper Polisher Pipeline

What it solves

Paper plagiarism reduction often runs into a conflict: lower the repetition rate without breaking academic meaning, citations, formulas, and terminology. Many AI-assisted paragraphs also carry obvious machine-like patterns, such as overuse of crucial, delve, interplay, verbose substitutes for is, or forced three-part structures. Simple synonym replacement can damage technical terms, distort argumentation, or remove necessary source markers.

How it works

The skill treats polishing as a three-stage pipeline:

• Plagiarism reduction: rewrites using synonym substitution, sentence restructuring, voice switching, word-order adjustment, and perspective changes. It protects domain terms, direct quotations, formulas, data, figure/table numbers, and references, and asks for a strategy table mapping original spans, rewritten spans, and the tactic used.
• AI-trace removal: detects common AI writing signs, including inflated importance, vague prestige claims, ad-like adjectives, em-dash overuse, As of [date] disclaimers, not-only-but patterns, and forced triads. The rewrite keeps academic restraint instead of adding decorative language.
• Word output: uses python-docx to generate a structured report with A4 layout, heading levels, line spacing, table styling, and page-number footer, while color-coding pre/post reduction and humanization edits.

The boundary is clear: it performs language-level refinement only. It does not write the thesis, invent academic content, remove citation markers, or guarantee a target duplication rate. It is best used on 300–500-word paragraphs first, especially in literature reviews or theoretical sections with high repetition.