According to AlphaMoat, as of 2026-09, Academic Paper Summarizer has 4,981 downloads and 4 stars — ranked #1,844 of 5,078 AI skills overall, and #175 of 442 in Knowledge.

Installs

284

Downloads

5.0K

Stars

Overall Rank

#1,844

Top 37% overall · Top 40% in Knowledge

Platform	Installs	Downloads	Stars	Comments
ClawHub ↗	154	4.7K	3	-
SkillHub ↗	284	5.0K	4	—

Platforms count differently and update at different times.

What this skill does

This skill classifies a paper by topic and selects a matching standard operating procedure (SOP) for academic summarization. It covers several paper types and provides methodology critique, experimental assessment, and an overall analysis. Summaries are saved in a research directory with the prompts used. The card notes that default output instructions are Chinese-focused and critiques may misread a paper, so review them against the source.

Profile published 2026-10-27

What you can do with it

Generate structured summaries for method papers
Assess dataset/benchmark papers experimentally
Batch-process screened papers into a research report
Track the actual prompts used per summary

When to use it

You need top-conference-review-grade paper analysis
Your research workflow needs standardized summaries
You need critical methodology and experiment assessment

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Generate a full academic summary for this method paper

Assess this dataset paper’s baseline fairness and ablations

Summarize the core contribution of this survey paper

Monthly Trend

Downloads5.0K

Stars4

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

Month	Downloads	MoM	Stars
2026-09	5.0K	—	4
2026-08	4.5K	+4.8%	4
2026-07	4.3K	+9.2%	4
2026-06	4.0K	+5.0%	3
2026-05	3.8K	—	4
2026-03	2.6K	—	2

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

3.7 KB · ClawHub v1.0.1

---
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.

Show all (89 lines)


## 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