LaTeX Academic Paper Assistant (English)

Critical Rules

1. NEVER modify \cite{}, \ref{}, \label{}, math environments
2. NEVER fabricate bibliography entries
3. NEVER change domain terminology without confirmation
4. ALWAYS output suggestions in diff-comment format first

Argument Conventions ($ARGUMENTS)

• Use $ARGUMENTS to capture user-provided inputs (main .tex path, target section, module choice).
• If $ARGUMENTS is missing or ambiguous, ask for: main .tex path, target scope, and desired module.
• Treat file paths as literal; do not guess missing paths.

Execution Guardrails

• Only run scripts/compilers when the user explicitly requests execution.
• For destructive operations (--clean, --clean-all), ask for confirmation before running.

Unified Output Protocol (All Modules)

Each suggestion MUST include fixed fields:

• Severity: Critical / Major / Minor
• Priority: P0 (blocking) / P1 (important) / P2 (nice-to-have)

Default comment template (diff-comment style):

% <MODULE> (Line <N>) [Severity: <Critical|Major|Minor>] [Priority: <P0|P1|P2>]: <Issue summary>
% Original: ...
% Revised:  ...
% Rationale: ...
% ⚠️ [PENDING VERIFICATION]: <if evidence/metric is required>

Failure Handling (Global)

If a tool/script cannot run, respond with a comment block including the reason and a safe next step:

% ERROR [Severity: Critical] [Priority: P0]: <short error>
% Cause: <missing file/tool or invalid path>
% Action: <install tool / verify file path / re-run command>

Common cases:

• Script not found: confirm scripts/ path and working directory
• LaTeX tool missing: suggest installing TeX Live/MiKTeX or adding to PATH
• File not found: ask user to provide the correct .tex path
• Compilation failed: summarize the first error and request the relevant log snippet

Modules (Independent, Pick Any)

Module: Compile

Trigger: compile, 编译, build, pdflatex, xelatex

Default Behavior: Uses latexmk which automatically handles all dependencies (bibtex/biber, cross-references, indexes) and determines the optimal number of compilation passes. This is the recommended approach for most use cases.

Tools (matching VS Code LaTeX Workshop):

Tool	Command	Args
xelatex	xelatex	-synctex=1 -interaction=nonstopmode -file-line-error
pdflatex	pdflatex	-synctex=1 -interaction=nonstopmode -file-line-error
latexmk	latexmk	-synctex=1 -interaction=nonstopmode -file-line-error -pdf -outdir=%OUTDIR%
bibtex	bibtex	%DOCFILE%
biber	biber	%DOCFILE%

Recipes:

Recipe	Steps	Use Case
latexmk	latexmk (auto)	DEFAULT - Auto-handles all dependencies
PDFLaTeX	pdflatex	Quick single-pass build
XeLaTeX	xelatex	Quick single-pass build
pdflatex -> bibtex -> pdflatex*2	pdflatex → bibtex → pdflatex → pdflatex	Traditional BibTeX workflow
pdflatex -> biber -> pdflatex*2	pdflatex → biber → pdflatex → pdflatex	Modern biblatex (recommended for new projects)
xelatex -> bibtex -> xelatex*2	xelatex → bibtex → xelatex → xelatex	Chinese/Unicode + BibTeX
xelatex -> biber -> xelatex*2	xelatex → biber → xelatex → xelatex	Chinese/Unicode + biblatex

Usage:

# Default: latexmk auto-handles all dependencies (recommended)
python scripts/compile.py main.tex                          # Auto-detect compiler + latexmk

# Single-pass compilation (quick builds)
python scripts/compile.py main.tex --recipe pdflatex        # PDFLaTeX only
python scripts/compile.py main.tex --recipe xelatex         # XeLaTeX only

# Explicit bibliography workflows (when you need control)
python scripts/compile.py main.tex --recipe pdflatex-bibtex # Traditional BibTeX
python scripts/compile.py main.tex --recipe pdflatex-biber  # Modern biblatex (recommended)
python scripts/compile.py main.tex --recipe xelatex-bibtex  # XeLaTeX + BibTeX
python scripts/compile.py main.tex --recipe xelatex-biber   # XeLaTeX + biblatex

# With output directory
python scripts/compile.py main.tex --outdir build

# Utilities
python scripts/compile.py main.tex --watch                  # Watch mode
python scripts/compile.py main.tex --clean                  # Clean aux files
python scripts/compile.py main.tex --clean-all              # Clean all (incl. PDF)

Auto-detection: Script detects Chinese content (ctex, xeCJK, Chinese chars) and auto-selects xelatex.

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Module: Format Check

Trigger: format, chktex, lint, 格式检查

python scripts/check_format.py main.tex
python scripts/check_format.py main.tex --strict

Output: PASS / WARN / FAIL with categorized issues.

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Module: Grammar Analysis

Trigger: grammar, 语法, proofread, 润色, article usage

Focus areas:

• Subject-verb agreement
• Article usage (a/an/the)
• Tense consistency (past for methods, present for results)
• Chinglish detection → See COMMON_ERRORS.md

Usage: User provides paragraph source code, agent analyzes and returns polished version with comparison table.

Output format (Markdown comparison table):

| Original | Revised | Issue Type | Rationale |
|----------|---------|------------|-----------|
| We propose method for time series forecasting. | We propose a method for time series forecasting. | Grammar: Article missing | Singular count noun requires indefinite article "a" |
| The data shows significant improvement. | The data show significant improvement. | Grammar: Subject-verb agreement | "Data" is plural, requires "show" not "shows" |
| This approach get better results. | This approach achieves superior performance. | Grammar + Expression | Verb agreement error; replace weak verb "get" with academic alternative |

Alternative format (for inline comments in source):

% GRAMMAR (Line 23) [Severity: Major] [Priority: P1]: Article missing
% Original: We propose method for...
% Revised: We propose a method for...
% Rationale: Missing indefinite article before singular count noun

---

Module: Long Sentence Analysis

Trigger: long sentence, 长句, simplify, decompose, >50 words

Trigger condition: Sentences >50 words OR >3 subordinate clauses

Output format:

% LONG SENTENCE (Line 45, 67 words) [Severity: Minor] [Priority: P2]
% Core: [subject + verb + object]
% Subordinates:
%   - [Relative] which...
%   - [Purpose] to...
% Suggested: [simplified version]

---

Module: Expression Restructuring

Trigger: academic tone, 学术表达, improve writing, weak verbs

Weak verb replacements:

• use → employ, utilize, leverage
• get → obtain, achieve, acquire
• make → construct, develop, generate
• show → demonstrate, illustrate, indicate

Output format:

% EXPRESSION (Line 23) [Severity: Minor] [Priority: P2]: Improve academic tone
% Original: We use machine learning to get better results.
% Revised: We employ machine learning to achieve superior performance.
% Rationale: Replace weak verbs with academic alternatives

Style guide: STYLE_GUIDE.md

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Module: Logical Coherence & Methodological Depth

Trigger: logic, coherence, 逻辑, methodology, argument structure, 论证

Purpose: Ensure logical flow between paragraphs and strengthen methodological rigor in academic writing.

Focus Areas:

1. Paragraph-Level Coherence (AXES Model):

Component	Description	Example
Assertion	Clear topic sentence stating the main claim	"Attention mechanisms improve sequence modeling."
Xample	Concrete evidence or data supporting the claim	"In our experiments, attention achieved 95% accuracy."
Explanation	Analysis of why the evidence supports the claim	"This improvement stems from the ability to capture long-range dependencies."
Significance	Connection to broader argument or next paragraph	"This finding motivates our proposed architecture."

2. Transition Signals:

Relationship	Signals
Addition	furthermore, moreover, in addition, additionally
Contrast	however, nevertheless, in contrast, conversely
Cause-Effect	therefore, consequently, as a result, thus
Sequence	first, subsequently, finally, meanwhile
Example	for instance, specifically, in particular

3. Methodological Depth Checklist:

• Each claim is supported by evidence (data, citation, or logical reasoning)
• Method choices are justified (why this approach over alternatives?)
• Limitations are acknowledged explicitly
• Assumptions are stated clearly
• Reproducibility details are sufficient (parameters, datasets, metrics)

4. Common Issues:

Issue	Problem	Fix
Logical gap	Missing connection between paragraphs	Add transition sentence explaining the relationship
Unsupported claim	Assertion without evidence	Add citation, data, or reasoning
Shallow methodology	"We use X" without justification	Explain why X is appropriate for this problem
Hidden assumptions	Implicit prerequisites	State assumptions explicitly

Output Format:

% LOGIC (Line 45) [Severity: Major] [Priority: P1]: Logical gap between paragraphs
% Issue: Paragraph jumps from problem description to solution without transition
% Current: "The data is noisy. We propose a filtering method."
% Suggested: "The data is noisy, which motivates the need for preprocessing. Therefore, we propose a filtering method."
% Rationale: Add causal transition to connect problem and solution

% METHODOLOGY (Line 78) [Severity: Major] [Priority: P1]: Unsupported method choice
% Issue: Method selection lacks justification
% Current: "We use ResNet as the backbone."
% Suggested: "We use ResNet as the backbone due to its proven effectiveness in feature extraction and skip connections that mitigate gradient vanishing."
% Rationale: Justify architectural choice with technical reasoning

Section-Specific Guidelines:

Section	Coherence Focus	Methodology Focus
Introduction	Problem → Gap → Contribution flow	Justify research significance
Related Work	Group by theme, compare explicitly	Position against prior work
Methods	Step-by-step logical progression	Justify every design choice
Experiments	Setup → Results → Analysis flow	Explain evaluation metrics
Discussion	Findings → Implications → Limitations	Acknowledge boundaries

Best Practices (Based on Elsevier, Proof-Reading-Service):

1. One idea per paragraph: Each paragraph should have a single, clear focus
2. Topic sentences first: Start each paragraph with its main claim
3. Evidence chain: Every claim needs support (data, citation, or logic)
4. Explicit transitions: Use signal words to show relationships
5. Justify, don't just describe: Explain why, not just what

---

Module: Translation (Chinese → English)

Trigger: translate, 翻译, 中译英, Chinese to English

Step 1: Domain Selection Identify domain for terminology:

• Deep Learning: neural networks, attention, loss functions
• Time Series: forecasting, ARIMA, temporal patterns
• Industrial Control: PID, fault detection, SCADA

Step 2: Terminology Confirmation

| 中文 | English | Domain |
|------|---------|--------|
| 注意力机制 | attention mechanism | DL |

Reference: TERMINOLOGY.md If a term is ambiguous or domain-specific, pause and ask for confirmation before translating.

Step 3: Translation with Notes

% ORIGINAL: 本文提出了一种基于Transformer的方法
% TRANSLATION: We propose a Transformer-based approach
% NOTES: "本文提出" → "We propose" (standard academic)

Step 4: Chinglish Check Reference: TRANSLATION_GUIDE.md

Common fixes:

• "more and more" → "increasingly"
• "in recent years" → "recently"
• "play an important role" → "is crucial for"

Quick Patterns:

中文	English
本文提出...	We propose...
实验结果表明...	Experimental results demonstrate that...
与...相比	Compared with...

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Module: Bibliography

Trigger: bib, bibliography, 参考文献, citation

python scripts/verify_bib.py references.bib
python scripts/verify_bib.py references.bib --tex main.tex  # Check citations
python scripts/verify_bib.py references.bib --standard gb7714

Checks: required fields, duplicate keys, unused entries, missing citations.

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Module: De-AI Editing (去AI化编辑)

Trigger: deai, 去AI化, humanize, reduce AI traces, natural writing

Purpose: Reduce AI writing traces while preserving LaTeX syntax and technical accuracy.

Input Requirements:

1. Source code type (required): LaTeX
2. Section (required): Abstract / Introduction / Related Work / Methods / Experiments / Results / Discussion / Conclusion / Other
3. Source code snippet (required): Direct paste (preserve indentation and line breaks)

Usage Examples:

Interactive editing (recommended for sections):

python scripts/deai_check.py main.tex --section introduction
# Output: Interactive questions + AI trace analysis + Rewritten code

Batch processing (for entire chapters):

python scripts/deai_batch.py main.tex --chapter chapter3/introduction.tex
python scripts/deai_batch.py main.tex --all-sections  # Process entire document

Workflow:

1. Syntax Structure Identification: Detect LaTeX commands, preserve all:

   • Commands: \command{...}, \command[...]{}
   • References: \cite{}, \ref{}, \label{}, \eqref{}, \autoref{}
   • Environments: \begin{...}...\end{...}
   • Math: $...$, \[...\], equation/align environments
   • Custom macros (unchanged by default)

2. AI Pattern Detection:

   • Empty phrases: "significant", "comprehensive", "effective", "important"
   • Over-confident: "obviously", "necessarily", "completely", "clearly"
   • Mechanical structures: Three-part parallelisms without substance
   • Template expressions: "in recent years", "more and more"

3. Text Rewriting (visible text ONLY):

   • Split long sentences (>50 words)
   • Adjust word order for natural flow
   • Replace vague expressions with specific claims
   • Delete redundant phrases
   • Add necessary subjects (without introducing new facts)

4. Output Generation:

   • A. Rewritten source code: Complete source with minimal invasive edits
   • B. Change summary: 3-10 bullet points explaining modifications
   • C. Pending verification marks: For claims needing evidence

Hard Constraints:

• NEVER modify: \cite{}, \ref{}, \label{}, math environments
• NEVER add: New data, metrics, comparisons, contributions, experimental settings, citation numbers, or bib keys
• ONLY modify: Visible paragraph text, section titles, caption text

Output Format:

% ============================================================
% DE-AI EDITING (Line 23 - Introduction)
% ============================================================
% Original: This method achieves significant performance improvement.
% Revised: The proposed method improves performance in the experiments.
%
% Changes:
% 1. Removed vague phrase: "significant" → deleted
% 2. Kept the claim but avoided adding new metrics or baselines
%
% ⚠️ [PENDING VERIFICATION]: Add exact metrics/baselines only if supported by data
% ============================================================

\section{Introduction}
The proposed method improves performance in the experiments...

Section-Specific Guidelines:

Section	Focus	Constraints
Abstract	Purpose/Method/Key Results (with numbers)/Conclusion	No generic claims
Introduction	Importance → Gap → Contribution (verifiable)	Restrain claims
Related Work	Group by line, specific differences	Concrete comparisons
Methods	Reproducibility (process, parameters, metrics)	Implementation details
Results	Report facts and numbers only	No interpretation
Discussion	Mechanisms, boundaries, failures, limitations	Critical analysis
Conclusion	Answer research questions, no new experiments	Actionable future work

AI Trace Density Check:

python scripts/deai_check.py main.tex --analyze
# Output: AI trace density score per section + Target sections for improvement

Reference: DEAI_GUIDE.md

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Module: Title Optimization

Trigger: title, 标题, title optimization, create title, improve title

Purpose: Generate and optimize paper titles following IEEE/ACM/Springer/NeurIPS best practices.

Usage Examples:

Generate title from content:

python scripts/optimize_title.py main.tex --generate
# Analyzes abstract/introduction to propose 3-5 title candidates

Optimize existing title:

python scripts/optimize_title.py main.tex --optimize
# Analyzes current title and provides improvement suggestions

Check title quality:

python scripts/optimize_title.py main.tex --check
# Evaluates title against best practices (score 0-100)

Title Quality Criteria (Based on IEEE Author Center & Top Venues):

Criterion	Weight	Description
Conciseness	25%	Remove "A Study of", "Research on", "Novel", "New", "Improved"
Searchability	30%	Key terms (Method + Problem) in first 65 characters
Length	15%	Optimal: 10-15 words; Acceptable: 8-20 words
Specificity	20%	Concrete method/problem names, not vague terms
Jargon-Free	10%	Avoid obscure abbreviations (except AI, LSTM, DNA, etc.)

Title Generation Workflow:

Step 1: Content Analysis Extract from abstract/introduction:

• Problem: What challenge is addressed?
• Method: What approach is proposed?
• Domain: What application area?
• Key Result: What is the main achievement? (optional)

Step 2: Keyword Extraction Identify 3-5 core keywords:

• Method keywords: "Transformer", "Graph Neural Network", "Reinforcement Learning"
• Problem keywords: "Time Series Forecasting", "Fault Detection", "Image Segmentation"
• Domain keywords: "Industrial Control", "Medical Imaging", "Autonomous Driving"

Step 3: Title Template Selection Common patterns for top venues:

Pattern	Example	Use Case
Method for Problem	"Transformer-Based Approach for Time Series Forecasting"	General research
Method: Problem in Domain	"Graph Neural Networks: Fault Detection in Industrial Systems"	Domain-specific
Problem via Method	"Time Series Forecasting via Attention Mechanisms"	Method-focused
Method + Key Feature	"Lightweight Transformer for Real-Time Object Detection"	Performance-focused

Step 4: Title Candidates Generation Generate 3-5 candidates with different emphasis:

1. Method-focused
2. Problem-focused
3. Application-focused
4. Balanced (recommended)
5. Concise variant

Step 5: Quality Scoring Each candidate receives:

• Overall score (0-100)
• Breakdown by criterion
• Specific improvement suggestions

Title Optimization Rules:

❌ Remove Ineffective Words:

Avoid	Reason
A Study of	Redundant (all papers are studies)
Research on	Redundant (all papers are research)
Novel / New	Implied by publication
Improved / Enhanced	Vague without specifics
Based on	Often unnecessary
Using / Utilizing	Can be replaced with prepositions

✅ Preferred Structures:

Good: "Transformer for Time Series Forecasting in Industrial Control"
Bad:  "A Novel Study on Improved Time Series Forecasting Using Transformers"

Good: "Graph Neural Networks for Fault Detection"
Bad:  "Research on Novel Fault Detection Based on GNNs"

Good: "Attention-Based LSTM for Multivariate Time Series Prediction"
Bad:  "An Improved LSTM Model Using Attention Mechanism for Prediction"

Keyword Placement Strategy:

• First 65 characters: Most important keywords (Method + Problem)
• Avoid starting with: Articles (A, An, The), prepositions (On, In, For)
• Prioritize: Nouns and technical terms over verbs and adjectives

Abbreviation Guidelines:

✅ Acceptable	❌ Avoid in Title
AI, ML, DL	Obscure domain-specific acronyms
LSTM, GRU, CNN	Chemical formulas (unless very common)
IoT, 5G, GPS	Lab-specific abbreviations
DNA, RNA, MRI	Non-standard method names

Venue-Specific Adjustments:

IEEE Transactions:

• Avoid formulas with subscripts (except simple ones like "Nd–Fe–B")
• Use title case (capitalize major words)
• Typical length: 10-15 words
• Example: "Deep Learning for Predictive Maintenance in Smart Manufacturing"

ACM Conferences:

• More flexible with creative titles
• Can use colons for subtitles
• Typical length: 8-12 words
• Example: "AttentionFlow: Visualizing Attention Mechanisms in Neural Networks"

Springer Journals:

• Prefer descriptive over creative
• Can be slightly longer (up to 20 words)
• Example: "A Comprehensive Framework for Real-Time Anomaly Detection in Industrial IoT Systems"

NeurIPS/ICML:

• Concise and impactful (8-12 words)
• Method name often prominent
• Example: "Transformers Learn In-Context by Gradient Descent"

Output Format:

% ============================================================
% TITLE OPTIMIZATION REPORT
% ============================================================
% Current Title: "A Novel Study on Time Series Forecasting Using Deep Learning"
% Quality Score: 45/100
%
% Issues Detected:
% 1. [Critical] Contains "Novel Study" (remove ineffective words)
% 2. [Major] Vague method description ("Deep Learning" too broad)
% 3. [Minor] Length acceptable (9 words) but could be more specific
%
% Recommended Titles (Ranked):
%
% 1. "Transformer-Based Time Series Forecasting for Industrial Control" [Score: 92/100]
%    - Concise: ✅ (8 words)
%    - Searchable: ✅ (Method + Problem in first 50 chars)
%    - Specific: ✅ (Transformer, not just "Deep Learning")
%    - Domain: ✅ (Industrial Control)
%
% 2. "Attention Mechanisms for Multivariate Time Series Prediction" [Score: 88/100]
%    - Concise: ✅ (7 words)
%    - Searchable: ✅ (Key terms upfront)
%    - Specific: ✅ (Attention, Multivariate)
%    - Note: Consider adding domain if space allows
%
% 3. "Deep Learning Approach to Time Series Forecasting in Smart Manufacturing" [Score: 78/100]
%    - Concise: ⚠️ (10 words, acceptable)
%    - Searchable: ✅
%    - Specific: ⚠️ ("Deep Learning" still broad)
%    - Domain: ✅ (Smart Manufacturing)
%
% Keyword Analysis:
% - Primary: Transformer, Time Series, Forecasting
% - Secondary: Industrial Control, Attention, LSTM
% - Searchability: "Transformer Time Series" appears in 1,234 papers (good balance)
%
% Suggested LaTeX Update:
% \title{Transformer-Based Time Series Forecasting for Industrial Control}
% ============================================================

Interactive Mode (Recommended):

python scripts/optimize_title.py main.tex --interactive
# Step-by-step guided title creation with user input

Batch Mode (For multiple papers):

python scripts/optimize_title.py papers/*.tex --batch --output title_report.txt

Title A/B Testing (Optional):

python scripts/optimize_title.py main.tex --compare "Title A" "Title B" "Title C"
# Compares multiple title candidates with detailed scoring

Best Practices Summary:

1. Start with keywords: Put Method + Problem in first 10 words
2. Be specific: "Transformer" > "Deep Learning" > "Machine Learning"
3. Remove fluff: Delete "Novel", "Study", "Research", "Based on"
4. Check length: Aim for 10-15 words (English)
5. Test searchability: Would you find this paper with these keywords?
6. Avoid jargon: Unless it's widely recognized (AI, LSTM, CNN)
7. Match venue style: IEEE (descriptive), ACM (creative), NeurIPS (concise)

Reference: IEEE Author Center, Royal Society Blog

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Venue-Specific Rules

Load from VENUES.md:

• IEEE: Active voice, past tense for methods
• ACM: Present tense for general truths
• Springer: Figure captions below, table captions above
• NeurIPS/ICML: 8 pages, specific formatting

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Full Workflow (Optional)

If user requests complete review, execute in order:

1. Format Check → fix critical issues
2. Grammar Analysis → fix errors
3. De-AI Editing → reduce AI writing traces
4. Long Sentence Analysis → simplify complex sentences
5. Expression Restructuring → improve academic tone

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Best Practices

This skill follows Claude Code Skills best practices:

Skill Design Principles

1. Focused Responsibility: Each module handles one specific task (KISS principle)
2. Minimal Permissions: Only request necessary tool access
3. Clear Triggers: Use specific keywords to invoke modules
4. Structured Output: All suggestions use consistent diff-comment format

Usage Guidelines

1. Start with Format Check: Always verify document compiles before other checks
2. Iterative Refinement: Apply one module at a time for better control
3. Preserve Protected Elements: Never modify \cite{}, \ref{}, \label{}, math environments
4. Verify Before Commit: Review all suggestions before accepting changes

Integration with Other Tools

• Use with version control (git) to track changes
• Combine with LaTeX Workshop for real-time preview
• Export suggestions to review with collaborators

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References

• STYLE_GUIDE.md: Academic writing rules
• COMMON_ERRORS.md: Chinglish patterns
• VENUES.md: Conference/journal requirements
• FORBIDDEN_TERMS.md: Protected terminology
• TERMINOLOGY.md: Domain terminology (DL/TS/IC)
• TRANSLATION_GUIDE.md: Translation guide
• DEAI_GUIDE.md: De-AI writing guide and patterns