Skill: nomorecoding/paper-summarize-academic (version 1.0.1)

--- name: paper_summarize description: Academic paper summarization with dynamic SOP selection based on paper topic classification. Supports method, dataset, multimodal, and other paper types with rigorous analysis templates. author: Claude (克劳德) version: 1.0.1 --- # Paper Summarize Skill This skill provides academic-grade paper summarization with dynamic Standard Operating Procedure (SOP) selection based on paper topic classification. ## Capabilities - **Dynamic SOP Selection**: Automatically selects appropriate analysis template based on paper type (method, dataset, multimodal, etc.) - **Rigorous Analysis**: Follows top-tier conference review criteria (NeurIPS/ICML/ICLR/ACL) - **Structured Output**: Generates comprehensive summaries with methodology critique, experimental assessment, strengths/weaknesses - **Local File Storage**: Saves summaries to organized directory structure with proper naming - **Prompt Tracking**: Maintains record of actual prompts used for reproducibility - **Dataset Focus**: Explicit attention to training/evaluation datasets used in experiments ## Supported Paper Types - `method`: Algorithm/architecture papers - `dataset`: Dataset/benchmark papers - `multimodal`: Cross-modal learning papers - `tech_report`: System/model release papers - `application`: Applied AI papers - `survey`: Survey/review papers - `rl_alignment`: RL/Alignment/Safety papers - `speech_audio`: Speech/audio processing papers - `benchmark`: Evaluation/benchmark papers - `analysis`: Empirical analysis papers ## Usage ### Input Requirements - Paper title, authors, abstract - Topic classification (one of supported types) - Research context (keywords, subtopics) ### Output Format - **Local file**: `{paper_title}.md` in `research/{domain}/ai_summaries/` - **Content structure**: - Paper information (title, authors, venue, links) - Core contribution summary - Methodology critique (2000+ words) - Experimental assessment (1000+ words, with dataset focus) - Strengths and weaknesses - Critical questions for authors - Impact assessment ### Quality Standards - **Methodology Critique**: 2000+ characters, deep technical analysis including pipeline, novelty, mathematical principles, assumptions, prior art comparison, computational cost, and failure modes - **Experimental Assessment**: 1000+ characters, rigorous evaluation with explicit focus on **datasets used for training and testing**, protocol rigor, baseline fairness, ablation completeness, and statistical significance - **Overall Analysis**: 3000+ characters, critical perspective - **Technical Precision**: Correct terminology, specific method names, exact metrics ## Workflow Integration This skill integrates with the broader research workflow: 1. **Paper Discovery**: Works with arXiv search results 2. **Quality Filtering**: Processes papers that pass relevance screening 3. **Batch Processing**: Can be called repeatedly for multiple papers 4. **Report Generation**: Outputs feed into final research report ## Configuration SOP templates are defined in: - `src/lib/agents/topic-sops.ts` (primary location) - `summarization_prompt.ts` (backup/reference) Both files contain identical SOP definitions with shared output format requirements. ## Examples ```bash # Summarize a method paper paper_summarize --title "SongEcho: Cover Song Generation" --topic "method" --abstract "..." --authors "..." # Summarize a dataset paper paper_summarize --title "MusicSem: Language-Audio Dataset" --topic "dataset" --abstract "..." --authors "..." ``` ## Files Created - `research/{domain}/ai_summaries/{paper_title}.md` - `research/{domain}/prompts/{paper_title}_prompt.txt` - Directory structure automatically created if missing

path: README.md
content: # Paper Summarize Skill

Academic paper summarization with dynamic SOP selection based on paper topic classification.

## Directory Structure

```
paper_summarize/
├── SKILL.md          # Skill definition and usage
├── README.md         # This file
├── scripts/          # Executable scripts (if any)
└── templates/        # SOP templates and prompt files
```

## Integration

This skill is designed to work within the broader music generation research workflow:

1. **Input**: Papers that have passed quality filtering
2. **Processing**: Applies appropriate SOP template based on topic classification
3. **Output**: Saves detailed summaries to organized local directory structure
4. **Tracking**: Maintains prompt records for reproducibility

## Usage in Research Workflow

When processing papers from arXiv search results:

```bash
# Example workflow integration
for paper in filtered_papers:
    topic = classify_paper(paper)  # method, dataset, multimodal, etc.
    summary = paper_summarize(
        title=paper.title,
        authors=paper.authors, 
        abstract=paper.abstract,
        topic=topic,
        domain="music_generation"
    )
    save_to_file(summary, f"research/music_generation/ai_summaries/{paper.title}.md")
```

## Quality Assurance

- All summaries follow rigorous academic standards
- Methodology critique receives extra emphasis (1500+ characters minimum)
- Critical perspective maintained throughout (not just paper restatement)
- Technical precision with correct terminology and specific references

path: USAGE_EXAMPLE.md
content: # Paper Summarize Skill - Usage Example

## Step 1: Classify Paper Topic
```javascript
// Example paper classification
const papers = [
  {
    title: "SongEcho: Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation",
    topic: "method", // method, dataset, multimodal, etc.
    authors: "Sifei Li, Yang Li, Zizhou Wang, et al.",
    abstract: "Cover songs constitute a vital aspect of musical culture..."
  },
  {
    title: "MusicSem: A Semantically Rich Language--Audio Dataset",
    topic: "dataset",
    authors: "Rebecca Salganik, Teng Tu, Fei-Yueh Chen, et al.", 
    abstract: "We introduce MusicSem, a semantically rich language-audio dataset..."
  },
  {
    title: "Art2Mus: Artwork-to-Music Generation via Visual Conditioning",
    topic: "multimodal",
    authors: "Ivan Rinaldi, Matteo Mendula, Nicola Fanelli, et al.",
    abstract: "Art2Mus enables artwork-to-music generation through visual conditioning..."
  }
];
```

## Step 2: Apply Appropriate SOP Template
```javascript
// For each paper, select SOP based on topic
function getPaperSummary(paper) {
  const { title, authors, abstract, topic } = paper;
  
  // Load appropriate SOP template
  const sopTemplate = getSOPForTopic(topic);
  
  // Fill template variables
  const userPrompt = sopTemplate
    .replace('{{title}}', title)
    .replace('{{authors}}', authors)
    .replace('{{abstract}}', abstract)
    .replace('{{topic}}', topic);
    
  // Combine with system prompt
  const fullPrompt = SYSTEM_PROMPT + '\n\n' + userPrompt;
  
  return fullPrompt;
}
```

## Step 3: Generate and Save Summary
```javascript
// Generate summary using AI model
const summary = generateSummary(fullPrompt);

// Save to organized directory structure
const safeTitle = title.replace(/[^a-zA-Z0-9]/g, '_');
const filePath = `research/music_generation/ai_summaries/${safeTitle}.md`;
writeFile(filePath, summary);

// Save prompt for reproducibility
const promptPath = `research/music_generation/prompts/${safeTitle}_prompt.txt`;
writeFile(promptPath, fullPrompt);
```

## Expected Output Structure

### File: `SongEcho_Towards_Cover_Song_Generation_via_Instance-Adaptive_Element-wise_Linear_Modulation.md`
```markdown
# SongEcho: Towards Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation

## 论文信息
- **标题**: SongEcho: Towards Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation
- **作者**: Sifei Li, Yang Li, Zizhou Wang, Yuxin Zhang, Fuzhang Wu, Oliver Deussen, Tong-Yee Lee, Weiming Dong
- **机构**: 中国科学院自动化研究所、中国科学院大学人工智能学院、中国科学院软件研究所、康斯坦茨大学、国立成功大学
- **发表**: ICLR 2026
- **arXiv**: [2602.19976](https://arxiv.org/abs/2602.19976)
- **代码**: [GitHub](https://github.com/lsfhuihuiff/SongEcho_ICLR2026)

## 核心贡献
[1500+字的方法论深度剖析]
[1000+字的实验评估]
[完整的分析正文，总计3000+字]
```

## Quality Assurance Checklist

- [ ] Methodology critique ≥ 1500 characters
- [ ] Experimental assessment ≥ 1000 characters  
- [ ] Total analysis ≥ 3000 characters
- [ ] Critical perspective (not just restatement)
- [ ] Technical precision with correct terminology
- [ ] Proper file naming and directory structure
- [ ] Prompt saved for reproducibility

path: _meta.json
content: slug: paper-summarize-academic
version: 1.0.1
description: Academic paper summarization with dynamic SOP selection based on paper topic classification. Supports method, dataset, multimodal, and other paper types with rigorous analysis templates.

path: templates/sop_templates.ts
content: /**
 * Topic-specific SOP (Standard Operating Procedure) templates for paper analysis.
 *
 * Each SOP is modeled after top-tier ML conference review criteria
 * (NeurIPS / ICML / ICLR / ACL reviewing guidelines).
 *
 * Template variables: {{title}}, {{authors}}, {{abstract}}, {{topic}}, {{keywords}}, {{subtopics}}, {{paper_scope}}
 */
const SHARED_OUTPUT_FORMAT = `
## 输出格式（严格 JSON，不要 markdown code block 包裹）
{
  "tldr": "一句话概括核心贡献（学术风格，可中英混合，~50字）",
  "analysis": "完整的深度分析正文（Markdown 格式，按上述维度用 ## 子标题分段展开，每个维度 300+ 字，总计 3000+ 字。使用精确的技术术语，引用具体的数值/方法名。分析需要有批判性视角，而非简单复述。）",
  "methodology_critique": "**方法论深度剖析（2000+ 字，本字段是重点！）**：
  1. 技术方案的完整 pipeline 描述（输入→处理→输出）
  2. 核心创新点的 technical novelty（区分 genuine novelty vs. incremental engineering vs. straightforward combination）
  3. 关键算法/模块的数学原理或设计 intuition（如有公式，用 LaTeX 表示）
  4. 关键假设及其合理性分析
  5. 与最相关 prior art 的本质区别（不只是性能差异，而是思路层面的差异）
  6. Computational cost 和 scalability 评估
  7. 方法的 failure modes 和适用边界",
  "experimental_assessment": "实验评估（1000+ 字）：使用了哪些数据集进行训练和测试、experimental protocol 的严谨性、baseline 选择的公平性、ablation 的完整性、statistical significance、关键数值结果对比、结果是否支撑 claims",
  "strengths": "主要优势（逐条列出 4-6 条，每条 1-2 句，reviewer 风格：具体指出 what 和 why）",
  "weaknesses": "主要不足（逐条列出 3-5 条，每条 1-2 句，指出具体 technical concern，不泛泛而谈）",
  "questions": "对作者的关键追问（4-6 个，如同 peer review 中 Questions for Authors，应涵盖 methodology、experiments、reproducibility 各方面）",
  "significance": "影响力评估（3-4 句：对领域的 short-term 和 long-term 影响、practical implications、以及对后续研究的启发方向）"
}`;

export const SYSTEM_PROMPT = `You are a senior ML/AI researcher serving as an Area Chair at a top-tier venue (NeurIPS/ICML/ICLR). You are conducting a thorough, critical, and constructive analysis of a submitted paper. Your analysis should:
1. Be technically precise — use correct terminology, reference specific methods/equations/results
2. Be critical but constructive — identify genuine strengths AND concrete weaknesses
3. Distinguish novelty from engineering effort
4. Assess whether claims are adequately supported by evidence
5. Evaluate experimental rigor (baselines, ablations, statistical significance)
6. Consider reproducibility and broader impact
7. **Place EXTRA emphasis on methodology analysis** — the methodology_critique field should be the most detailed section, providing a deep dive into the technical approach, algorithm design, mathematical formulation, and key innovations. This is the most valuable part for researchers.

Write your analysis in Chinese (中文), but keep technical terms, method names, and metrics in English. Use Markdown formatting with headers, bullet points, and LaTeX formulas where appropriate. Use a professional academic tone throughout.

IMPORTANT: Your output MUST be valid JSON. Do NOT wrap it in markdown code blocks. The methodology_critique field should be at least 600 characters.`;

// ... rest of TOPIC_SOPS would follow the same pattern as in summarization_prompt.ts

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