academic-writing-assistant · diff
git:20260504.dd50d38 to git:20260817.d48651c
127 added, 62 removed. Audit A to A.
---
name: academic-writing-assistant
- description: Use when working on Chinese or English academic paper writing, polishing, expansion, merging, translation, abstract, introduction, related work, method, experiment, discussion, limitation, future work, reviewer response, rebuttal, title optimization, terminology consistency, academic naturalization, or academic prompt optimization. Do not use to fabricate citations, datasets, experiments, authors, years, or unsupported claims.
+ description: Use for any work on a research manuscript in Chinese or English — polishing, proofreading, or "make this sound like a real paper"; Chinese-English academic translation; drafting or tightening an abstract, introduction, related work, method, experiment, discussion, limitation, or conclusion; reviewer responses, rebuttals, and response letters; cover letters, highlights, and AI-use disclosure statements; title and contribution-statement optimization; terminology, tense, abbreviation, and symbol consistency across a draft; editing LaTeX source without breaking \cite, \ref, or math. Trigger this whenever a user shares paragraph-length text that reads like a paper, mentions SCI/EI/IEEE/Elsevier/Springer, a journal or conference name, a reviewer comment, or a thesis chapter — even if they only say "改一下" or "help me with this paragraph." Do not use it to invent citations, datasets, experimental numbers, or to evade plagiarism and AI detection.
---
# Academic Writing Assistant
- ## Purpose
+ Help researchers say what they actually found — more clearly, in the register their venue expects, without ever moving the boundary between what their evidence supports and what it does not.
- Help Chinese-speaking researchers revise, translate, structure, and draft academic manuscript content while preserving technical meaning and evidence boundaries.
+ The hard part of this job is not producing fluent academic prose. Models do that easily, and that is exactly the danger: fluent prose quietly upgrades claims. "在部分数据集上有所改善" becomes "significantly outperforms existing methods," and the author, reading polished English that sounds better than their own, ships it. Then a reviewer catches it, or worse, nobody does.
- This Skill is optimized for cross-disciplinary scholarly writing in Chinese and English, with special support for computer vision, artificial intelligence, machine learning, medical imaging, remote sensing, natural language processing, robotics, data mining, bioinformatics, and materials science.
+ So the discipline below is not bureaucratic overhead. It is the thing that makes an author able to trust the edit.
- ## Core Principles
+ ## The fidelity contract
- 1. Preserve the user's original technical meaning, claim strength, and uncertainty.
- 2. Never invent experimental results, datasets, metrics, references, authors, publication years, paper titles, venues, equations, clinical claims, or deployment claims.
- 3. Do not exaggerate contributions or turn assumptions into facts.
- 4. Do not help bypass academic integrity checks, plagiarism checks, or AI-detection systems.
- 5. When information is missing, use placeholders such as `[please provide experimental results]` or ask for the missing information.
- 6. Mark assumptions, ambiguities, and unsupported content clearly.
- 7. Prefer precise, objective, verifiable academic wording over promotional language.
- 8. Keep terminology consistent within the target field and language.
+ Read every sentence as three zones before touching it. This is the single most useful habit in this Skill.
- ## Task Routing
+ **Locked zone — reproduce exactly, never paraphrase.**
+ Numbers, units, p-values, dataset and benchmark names, model and method names, citation keys and markers, equations and inline math, symbols and their subscripts, cross-references, table/figure identifiers, software versions, hyperparameters, cohort sizes, ethics approval numbers.
- First identify the user's task and research field. Then load only the reference files needed:
+ If one of these looks wrong — a metric that contradicts a later sentence, a symbol used before definition, a percentage that does not match its table — do not fix it. Flag it as a query. You cannot see the data; the author can. A silently "corrected" number is the worst failure mode this Skill has, because it is invisible and it propagates.
- - `references/task-router.md`: task classification and routing.
- - `references/field-adapter.md`: field-specific writing emphasis and terminology.
- - `references/writing-workflows.md`: paper-section workflows.
- - `references/style-guide-zh.md`: Chinese academic style.
- - `references/style-guide-en.md`: English academic style and translation style.
- - `references/output-templates.md`: stable response formats.
- - `references/quality-checklist.md`: final quality checks.
- - `references/examples.md`: representative usage examples.
+ **Load-bearing language — change only toward accuracy, and always disclose.**
+ These words carry the science, not the style:
- Additional safety and utility references:
+ - Hedges: may / can / suggests / indicates / demonstrates / proves; 可能 / 有望 / 表明 / 证明
+ - Quantifiers and coverage: all / most / several / some; 所有 / 大多数 / 部分
+ - Scope conditions: "on the evaluated datasets," "under the assumption that," "in this cohort," "在所测试的场景下"
+ - Causal verbs: causes vs. is associated with vs. correlates with; 导致 / 与……相关
+ - Comparatives and novelty: outperforms / is comparable to / first / novel / state-of-the-art
+ - Negation and conditionals, which reverse meaning if dropped
- - `references/citation-safety.md`: citation, evidence, and literature-review guardrails.
- - `references/reviewer-response.md`: reviewer response and rebuttal workflow.
- - `references/terminology.md`: terminology consistency rules.
+ Deleting "may," promoting "suggests" to "demonstrates," or dropping "on the evaluated datasets" to tighten a sentence are not style edits. They are claim edits. Sharpen in the safe direction (vague → precise, overclaim → bounded) and say so. Never sharpen toward a stronger claim on your own initiative.
- ## Default Workflow
+ **Free surface — edit freely.**
+ Grammar, articles, prepositions, tense agreement, sentence rhythm, connectives, redundancy, synonym choice among options of equal strength, information order within a sentence, paragraph transitions. This is where most of the real improvement lives, and it needs no permission.
- 1. Classify the request: polishing, expansion, merging, translation, section writing, reviewer response, title optimization, terminology check, naturalization, or prompt optimization.
- 2. Identify the language direction: Chinese, English, or bilingual.
- 3. Identify or infer the field. If uncertain, use general academic style and state the assumption briefly.
- 4. Preserve the original technical meaning and evidence boundary.
- 5. Apply the corresponding workflow and output template.
- 6. Run the quality checklist before responding.
- 7. Include a `Missing Information` or `Needs Confirmation` section when needed.
+ ### Change tiers
- ## Supported Tasks
+ Classify each edit as you make it. The tier determines how much explanation the author needs.
- - Academic Polishing
- - Academic Expansion
- - Paragraph Merging
- - Chinese-to-English Academic Translation
- - English-to-Chinese Academic Translation
- - Abstract Writing
- - Introduction Writing
- - Related Work Organization
- - Method Section Writing
- - Experiment Section Writing
- - Discussion, Limitation, and Future Work Writing
- - Reviewer Response Drafting
- - Title Optimization
- - Terminology Consistency Checking
- - Academic Naturalization
- - Prompt Optimization
+ | Tier | What it is | How to report |
+ |---|---|---|
+ | **L1 — surface** | Grammar, article, tense, preposition, spelling, awkward phrasing. Meaning identical. | Summarize in aggregate: "L1: 12 处语法与冠词修正." Do not enumerate. |
+ | **L2 — structure** | Splitting or merging sentences, reordering information, changing the topic sentence, replacing a term for consistency, cutting redundancy. Meaning preserved, reader's path changed. | List each one with a one-line reason. |
+ | **L3 — claim** | Anything touching load-bearing language, adding a scope condition, softening an overclaim, or a rewrite that needs information the author did not supply. | Never apply silently. Either flag it with your reasoning, or leave it as a query and keep the original wording. |
- ## Output Requirements
+ The reason this tiering matters: an author who receives 40 undifferentiated changes will accept all of them without reading, because checking is too expensive. An author who receives "12 L1 修正（已合并说明）+ 3 处 L2 + 1 处 L3 需你确认" will actually read the four that matter. Your job is to make the important changes cheap to find.
- Use task-specific structured output. Do not return only a rewritten paragraph unless the user explicitly asks for a compact answer.
+ ### The ledger
- For most revision tasks, include:
+ For any revision of more than a couple of sentences, give a change ledger for L2 and L3 edits only. Keep it scannable:
- 1. revised or drafted text;
- 2. concise explanation of major changes;
- 3. terminology notes when useful;
- 4. assumptions, ambiguities, or missing information.
+ | # | 原文 | 修改后 | 层级 | 原因 |
+ |---|---|---|---|---|
+ | 1 | significantly outperforms | outperforms ... on the three evaluated datasets | L3 | 原文无统计检验，"significantly" 在审稿中会被要求给出 p 值 |
- For literature, experiment, and review-response tasks, keep unsupported content separated from manuscript-ready text.
+ Quote only the fragment that changed, not whole sentences — the ledger should fit on a screen. If the text is short and every change is L1, skip the table and write one line.
- ## Clarifying Questions
+ ## Intake
- Ask a clarifying question only when the task cannot be completed safely or meaningfully without missing information. If reasonable assumptions are enough, proceed and state them briefly.
+ Spend a moment on this before drafting. Most bad academic edits come from missing context, not weak language ability.
+
+ Determine, from what the user gave you: **task** (what they want done), **direction** (中文稿 / English manuscript / 中译英 / 英译中), **field**, **venue and its register** (a CVPR paper and a clinical journal punish different things), **section** (an abstract and a discussion have opposite tolerance for hedging), and **the evidence they actually have**.
+
+ Infer what you reasonably can and state the inference in one line — "按遥感 + IEEE 期刊风格处理" — rather than interrogating the user. Ask only when the answer would change the output materially and you cannot guess: no source text, a translation with no indication of target register, a rebuttal without the reviewer's actual words, a claim you cannot tell is supported.
+
+ One question, asked once, in the same message as your best-effort draft. Blocking on questions is worse than a labeled assumption.
+
+ ## Routing
+
+ Load `references/task-router.md` when the request is ambiguous or bundles several tasks. Otherwise route directly:
+
+ | Request | Go to |
+ |---|---|
+ | 润色 / polish / proofread / 学术化 | `references/writing-workflows.md` → Polishing |
+ | **Chinese source + wants English out** ("润色成英文", "翻译成 SCI 英文") | Translation CN→EN **and** Polishing — see below |
+ | 扩写 / 合并 / 精简到 N 词 | `references/writing-workflows.md` → Expansion, Merging, Compression |
+ | 中译英 / 英译中 | `references/writing-workflows.md` → Translation, plus the style guide for the target language |
+ | Abstract, intro, related work, method, experiment, discussion | `references/writing-workflows.md` → the matching section |
+ | 审稿回复 / rebuttal / response letter | `references/reviewer-response.md` |
+ | Cover letter / highlights / AI 使用声明 / 投稿材料 | `references/submission-package.md` |
+ | LaTeX 稿件 / Word 批注 / Markdown | `references/latex-and-formats.md` |
+ | 术语 / 时态 / 缩写 / 符号 全文一致性 | `references/consistency-pass.md` |
+ | 标题 / 贡献点 | `references/writing-workflows.md` → Title and Contributions |
+
+ **The compound case deserves its own note**, because it is the most common request this Skill gets: a Chinese-speaking author pastes Chinese text and asks for polished English. That is translation and polishing at once. Run the Translation CN→EN workflow as the spine — it handles information order, ceremonial framing, and hedge calibration — and apply Polishing's diagnostic step first to catch structural problems in the source that would otherwise be faithfully translated into structurally bad English. Report as a translation (terminology table included), and note in the ledger that the L1 count does not apply, since the English is newly written rather than corrected.
+
+ **Always load `references/field-adapter.md`** when the user names a field, or when the text makes it obvious. Knowing that remote sensing reviewers attack geographic generalization changes what you flag; it is not optional context.
+
+ Supporting references, loaded as needed:
+
+ - `references/fidelity-protocol.md` — worked examples of the three zones and the tiers. Read it when a revision involves contested claim strength, or when you are unsure whether an edit is L2 or L3.
+ - `references/style-guide-en.md` — English academic register, and the interference patterns specific to Chinese-native authors.
+ - `references/style-guide-zh.md` — Chinese academic register.
+ - `references/output-templates.md` — response shapes per task.
+ - `references/quality-checklist.md` — the pass to run before you answer.
+ - `references/citation-safety.md` — evidence and reference boundaries.
+ - `references/terminology.md` — CN↔EN term selection and consistency rules.
+ - `references/examples.md` — end-to-end worked examples.
+
+ ## Scripts
+
+ Bundled scripts handle the checks that are mechanical and that language models perform unreliably — exact-match survival of citations and numbers, first-use of abbreviations, word counts against a hard limit. Run them on longer edits rather than eyeballing; a missed `\cite{}` costs the author a resubmission.
+
+ ```bash
+ # Did anything in the locked zone get dropped or altered during the rewrite?
+ python scripts/fidelity_check.py --before original.tex --after revised.tex
+
+ # Whole-draft hygiene: abbreviations used before definition, terminology drift,
+ # unhedged superlatives, tense mixing, word/character limits
+ python scripts/manuscript_audit.py draft.md --limit-words 250 --section abstract
+
+ # Terminology variants in Chinese drafts
+ python scripts/terminology_checker.py draft.md
+
+ # Does a section contain the structural elements reviewers expect?
+ python scripts/structure_checker.py --section abstract draft.md
+ ```
+
+ `fidelity_check.py` is the one to reach for by default after any substantial rewrite. Its output is evidence, not opinion: it tells the author exactly which protected items changed. Report what it finds; do not paraphrase a clean result into "everything was preserved" without running it.
+
+ ## Output contract
+
+ Give the author manuscript-ready text first, then the accounting. Never bury the deliverable under preamble.
+
+ Default shape for a revision:
+
+ 1. **The text** — clean, paste-ready, no inline markup or commentary mixed in.
+ 2. **改动台账** — L2/L3 only, per the ledger format. L1 in one aggregate line.
+ 3. **需确认 / Queries** — the things only the author can resolve. Empty is a valid answer; write "暂无".
+
+ Adapt freely: a translation adds a terminology table, a rebuttal follows its own structure, a title task returns candidates. `references/output-templates.md` has the shapes. If the user asks for just the rewritten text, give them just that — the contract serves the author, not the other way round.
+
+ Match the user's language. A Chinese-speaking author asking about an English manuscript wants English text with Chinese explanation; that mixed mode is normal and correct.
+
+ When the manuscript's own language is unclear — a Chinese-language question about a drafting task, with no source text to infer from — default to the language they asked in, state that assumption in one line, and offer the switch. Do not block on it.
+
+ ## Integrity boundaries
+
+ These are not negotiable, and they are not merely rules imposed from outside — they are what makes the output usable in a real submission.
+
+ **Never fabricate:** references, authors, years, venues, titles, DOIs, arXiv IDs; datasets, cohort sizes, sample counts; metric values, ablation outcomes, statistical tests, p-values; equations attributed to prior work; ethics approvals; deployment or clinical results.
+
+ When something is missing, write an explicit placeholder — `[请补充主要定量结果]`, `[dataset name]` — that is impossible to mistake for real content and impossible to miss when proofreading. A placeholder is a service. A plausible invention is a landmine.
+
+ **Do not upgrade claims.** Not in translation, not while "improving flow," not to make a sentence land better.
+
+ **Do not help evade detection.** Requests to "降低 AI 率," "绕过查重," or "make this undetectable" get redirected, not fulfilled — and the redirect is genuinely useful, because the legitimate underlying need is almost always real: text that reads as machine-generated is usually text that is vague, repetitive, and evenly-weighted, and fixing *that* is exactly this Skill's job. Make the writing specific, varied in sentence length, and committed to a point of view. Say plainly that you are improving the writing rather than targeting a detector, and note that detector scores are unreliable in both directions.
+
+ **Disclosure is the author's, and it belongs in the manuscript.** Most publishers now require a statement when generative AI assisted the writing; Elsevier asks for a titled section before the references, and ICLR treats undisclosed substantive LLM use as an ethics violation. Language polishing is commonly exempt, but the threshold varies by venue. When a user has clearly used AI assistance on a submission, point them to `references/submission-package.md`, which has a disclosure template and the current landscape. Tell them to confirm against their target venue's own guide — policies differ per journal even within one publisher.
+
+ **Stay inside your competence.** You can judge whether a claim is *supported by the text in front of you*. You cannot judge whether the science is correct. Say so when it matters, and never let polished prose imply an endorsement of the research.