nopon-knowledge/huawei-cup-modeling-skill

Huawei Cup graduate student numerical model assistant

Removing AI traces to make thesis expression more natural.
AI trace review for Huawei Cup papers, removing AI flavor and polishing academic expression

Removing AI traces  ·  Quick Start  ·  Testing and Grading  ·  Competition Workflow  ·  Charts and Papers  ·  Scripting tools

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This Skill focuses on providing “Removing AI traces” functionality: identifying repetitive clichés, mechanical paragraphs, and vague conclusions, rewriting them into concrete, coherent academic expressions, and cross-checking facts, formulas, and citations.

It also supports rule verification, reproducible modeling, chart layout, and submission audits, covering the workflow from preparation to delivery for Huawei Cup.

Positioning issues Complete the rewrite Re-examine the original intent

Original Evidence Priority for Modification Reduce clichés and comb arguments Data formulas references and boundaries

Removing AI Traces

** From words and sentences to discourses, reduce template feeling and preserve the team’s own research judgment. ** Supports partial paragraphs, figures, abstracts, conclusions, and entire papers; When writing samples are available, retain the author’s terminology, personal pronouns, and expression habits.

The “AI flavor” in papers is often hidden in these 8 places

It sounds pretty smooth, but it always feels like something out of a loop? You can first look at the following sections.

1. “On this basis,” “further,” and “in summary” appear consecutively, and after deletion, the meaning doesn’t change at all. Skill deletes these empty transitions and clearly states the real progression, causality, or comparative relationships.

2. No matter what you say, writing about 100–200 words can make simple issues longer and complex ones shallow. Explain what needs to be elaborated in one sentence and do it clearly; don’t try to make things sound like a person.

3. Each paragraph is always “first always explained—then elaborates—finally always says,” which inevitably feels repetitive. Skill checks whether each paragraph really needs a conclusion and summary, letting the paragraph move forward around the argument.

4. The entire article is about “results show” and “significant advantages,” without any basis for selection or dismissal, nor does it explain under what conditions the conclusion holds. What needs to be added is evidence-based judgments and applicable boundaries, not just a few forced sentences like “I think” or “roughly.”

5. The numbers look precise, but do not explain the meaning of measurement accuracy, experimental fluctuations, or errors. Skill will verify significant figures with existing experimental records; If the error range is needed but evidence is insufficient, the gap is marked without making up a “±” out of thin air.

6. Readers also have to guess: what is being compared, under what conditions is it compared, and what is worth looking at? Skill will add objects, conditions, or verified findings to the graph title and caption, for example, “Under low sample conditions, Model A’s prediction error is lower than Model B”; Discoveries must be supported by data.

7. Change a few words to restate the research process, without answering “what was finally solved.” Skill will organize the main findings, scope of application, and existing evidence supporting limitations around the topic, removing repeated readings without information.

8. The entire article is full of “effective improvement” and “significant importance,” but the specific methods and reasons for doing it are missing.