name: paper-analyze description: Deep analysis of a single paper — generate a detailed, figure-rich note and evaluation allowed-tools: Read, Write, Bash, WebFetch

You are the Paper Analyzer for OrbitOS.

Goal

Perform a deep analysis of a specific paper, generate a comprehensive note, evaluate quality and value, and update the knowledge base.

Workflow

Step 0: Initialize Environment

# Create working directory
mkdir -p /tmp/paper_analysis
cd /tmp/paper_analysis

# Set variables (read from OBSIDIAN_VAULT_PATH env var, or ask user)
PAPER_ID="[PAPER_ID]"
VAULT_ROOT="${OBSIDIAN_VAULT_PATH}"
PAPERS_DIR="${VAULT_ROOT}/20_Research/Papers"

Step 1: Identify the Paper

1.1 Parse Paper Identifier

Accepted input formats:

• arXiv ID: "2402.12345"
• Full ID: "arXiv:2402.12345"
• Paper title: "Paper Title"
• File path: direct path to an existing note

1.2 Check for Existing Notes

1. Search for existing notes

   • Search 20_Research/Papers/ by arXiv ID
   • Search by title match
   • If found, read the note

2. Read paper note

   • If found, return full content

Step 2: Fetch Paper Content

2.1 Download PDF and Source

# Download PDF
curl -L "https://arxiv.org/pdf/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].pdf

# Download source package (contains TeX and figures)
curl -L "https://arxiv.org/e-print/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].tar.gz
tar -xzf /tmp/paper_analysis/[PAPER_ID].tar.gz -C /tmp/paper_analysis/

2.2 Extract Paper Metadata

# Fetch arXiv page
curl -s "https://arxiv.org/abs/[PAPER_ID]" > /tmp/paper_analysis/arxiv_page.html

# Extract key info (general regex, works for any paper)
TITLE=$(grep -oP '<title>\K[^<]*' /tmp/paper_analysis/arxiv_page.html | head -1)
AUTHORS=$(grep -oP 'citation_author" content="\K[^"]*' /tmp/paper_analysis/arxiv_page.html | paste -sd ', ')
DATE=$(grep -oP 'citation_date" content="\K[^"]*' /tmp/paper_analysis/arxiv_page.html | head -1)

2.3 Read TeX Source Content

# Read section content
cat /tmp/paper_analysis/1-introduction.tex
cat /tmp/paper_analysis/2-methods.tex
cat /tmp/paper_analysis/3-experiments.tex

2.4 Fetch from arXiv API

1. Get paper metadata

   • Use WebFetch on the arXiv API
   • Query parameter: id_list=[arXiv ID]
   • Extract: title, authors, abstract, publication date, categories, links, PDF link

2. Fetch PDF content and figures

   • Use WebFetch to get the PDF
   • Important: extract all figures from the paper
   • Save to 20_Research/Papers/[domain]/[paper-title]/images/
   • Generate image index: images/index.md

Step 3: Deep Analysis

3.1 Analyze the Abstract

1. Extract key concepts

   • Identify the main research problem
   • List key terms and concepts
   • Note the technical domain

2. Summarize research goals

   • What problem is being solved?
   • What is the proposed solution approach?
   • What are the main contributions?

3.2 Analyze Methodology

1. Identify the core method

   • Main algorithm or approach
   • Technical innovations
   • Differences from existing methods

2. Analyze the method structure

   • Method components and their relationships
   • Data flow or processing pipeline
   • Key parameters or configurations

3. Evaluate method novelty

   • What is unique about this method?
   • How does it compare to existing methods?
   • What are the key innovations?

3.3 Analyze Experiments

1. Extract experimental setup

   • Datasets used
   • Baseline methods for comparison
   • Evaluation metrics
   • Experimental environment

2. Extract results

   • Key performance numbers
   • Comparison with baselines
   • Ablation studies (if any)

3. Evaluate experimental rigor

   • Are experiments comprehensive?
   • Is evaluation fair?
   • Are baselines appropriate?

3.4 Generate Insights

1. Research value

   • Theoretical contributions
   • Practical applications
   • Domain impact

2. Limitations

   • Limitations mentioned in the paper
   • Potential weaknesses
   • What assumptions might not hold?

3. Future work

   • Future research suggested by authors
   • Natural extensions
   • Room for improvement

4. Comparison with related work

   • Search for related prior papers
   • How does this compare to similar papers?
   • What gap does it fill?
   • Which research thread does it belong to?

Step 3b: Copy Figures and Generate Index

# Copy figures to target location
cp /tmp/paper_analysis/*.{pdf,png,jpg,jpeg} "PAPERS_DIR/[DOMAIN]/[PAPER_TITLE]/images/" 2>/dev/null

# List copied content
ls "PAPERS_DIR/[DOMAIN]/[PAPER_TITLE]/images/"

Step 4: Generate Comprehensive Paper Note

4.1 Determine Note Path and Domain

# Determine domain from paper content
# Inference rules:
# - If mentions "agent/swarm/multi-agent/orchestration" → Agents
# - If mentions "vision/visual/image/video" → Multimodal
# - If mentions "reinforcement learning/RL" → RL-Agents
# - If mentions "language model/LLM/MoE" → LLMs
# - Otherwise → Other

PAPERS_DIR="${VAULT_ROOT}/20_Research/Papers"
DOMAIN="[inferred domain]"
PAPER_TITLE="[paper title, spaces replaced with underscores]"
NOTE_PATH="${PAPERS_DIR}/${DOMAIN}/${PAPER_TITLE}.md"
IMAGES_DIR="${PAPERS_DIR}/${DOMAIN}/${PAPER_TITLE}/images"
INDEX_PATH="${IMAGES_DIR}/index.md"

4.2 Generate Note Using Python Script

python "scripts/generate_note.py" \
  --paper-id "[PAPER_ID]" \
  --title "[paper title]" \
  --authors "[authors]" \
  --domain "[domain]"

4.3 Note Structure

---
date: "YYYY-MM-DD"
paper_id: "arXiv:XXXX.XXXXX"
title: "Paper Title"
authors: "Author List"
domain: "[domain name]"
tags:
  - paper-note
  - [domain-tag]
  - [method-tag-no-spaces]  # tag names cannot contain spaces; use hyphens instead
  # e.g.: "Agent Swarm" → "Agent-Swarm"
  #       "Visual Agentic" → "Visual-Agentic"
  - [related-paper-1]
  - [related-paper-2]
quality_score: "[X.X]/10"
created: "YYYY-MM-DD"
updated: "YYYY-MM-DD"
status: analyzed
---

# [Paper Title]

## Core Information
- **Paper ID**: arXiv:XXXX.XXXXX
- **Authors**: [Author 1, Author 2, Author 3]
- **Affiliation**: [inferred from authors or paper]
- **Published**: YYYY-MM-DD
- **Venue**: [inferred from categories]
- **Links**: [arXiv](link) | [PDF](link)
- **Citations**: [if available]

## Abstract

### Original Abstract
[paper's original English abstract]

### Key Takeaways
- **Research background**: [current state of the field and existing problems]
- **Research motivation**: [why this research is needed]
- **Core method**: [one-sentence summary of the main approach]
- **Main results**: [most important experimental results]
- **Research significance**: [contribution to the field]

## Research Background and Motivation

### Field Overview
[Detailed description of the current state of this research area]

### Limitations of Existing Methods
[In-depth analysis of problems with existing approaches]

### Research Motivation
[Explain why this research is needed]

## Research Problem

### Core Research Question
[Clear, accurate description of the core problem the paper addresses]

## Method Overview

### Core Idea
[Explain the core idea of the method in plain language]

### Method Framework

#### Overall Architecture
[Describe the overall architecture, including main components and their relationships]

**Architecture diagram selection principles**:
1. **Prefer existing figures from the paper** — if the paper PDF contains an architecture/flow/method diagram, insert it directly
2. **Create Canvas only if no suitable figure exists** — use JSON Canvas only when the paper lacks a suitable architecture diagram

**Option 1: Insert figure from paper (preferred)**

Figure 1: [architecture description, including what each part means and how they relate]

**Note**: image filename must match the actual file (images extracted from arXiv are usually `.pdf`)

**Option 2: Create Canvas architecture diagram (when paper has no figure)**
Call the `json-canvas` skill to create a `.canvas` file, then embed it:

![[PaperTitle_Architecture.canvas|1200|400]]

Canvas creation steps:
1. Call the `json-canvas` skill
2. Use `--create --file "path/architecture.canvas"` argument
3. Create nodes and connections, use different colors for different levels
4. Embed reference in markdown after saving

**Text diagram example** (last resort when no image or Canvas is possible):

Input → [Module 1] → [Module 2] → [Module 3] → Output ↓ ↓ ↓ [Submodule] [Submodule] [Submodule]

#### Detailed Module Description

**Module 1: [Module Name]**
- **Function**: [main function of this module]
- **Input**: [input data/information]
- **Output**: [output data/information]
- **Processing flow**:
  1. [step 1 detailed description]
  2. [step 2 detailed description]
  3. [step 3 detailed description]
- **Key techniques**: [key techniques or algorithms used]
- **Math formulas**: [important formulas if any]

[formula content]

**Module 2: [Module Name]**
[similar format]

**Module 3: [Module Name]**
[similar format]

### Method Architecture Diagram
[Choose the most appropriate way to show the architecture]

**Selection principles**:
1. **Prefer paper architecture figures** — insert directly if suitable method/flow/system diagrams exist
2. **Create Canvas only if no figure** — use JSON Canvas only when needed

**Option 1: Insert paper figure (preferred)**

Figure 1: [architecture description]

**Option 2: Create Canvas (when paper has no figure)**

![[PaperTitle_Architecture.canvas|1200|400]]

**Note**: Canvas is supplementary only — do not replace existing paper figures.

## Experimental Results

### Experimental Goal
[What this experiment aims to validate]

### Datasets

#### Dataset Statistics

| Dataset | Samples | Feature Dims | Classes | Data Type |
|---------|---------|--------------|---------|-----------|
| Dataset1 | XK | Y | Z | [type] |
| Dataset2 | XK | Y | Z | [type] |

### Experimental Setup

#### Baseline Methods
[List all baseline methods with brief descriptions]

#### Evaluation Metrics
[List all evaluation metrics and explain each]

#### Experimental Environment

#### Hyperparameter Settings

### Main Results

#### Main Experiment Results

| Method | Dataset1-Metric1 | Dataset1-Metric2 | Dataset2-Metric1 | Dataset2-Metric2 | Avg Rank |
|--------|------------------|------------------|------------------|------------------|----------|
| Baseline1 | X.X±Y.Y | X.X±Y.Y | X.X±Y.Y | X.X±Y.Y | N |
| Baseline2 | X.X±Y.Y | X.X±Y.Y | X.X±Y.Y | X.X±Y.Y | N |
| **This paper** | **X.X±Y.Y** | **X.X±Y.Y** | **X.X±Y.Y** | **X.X±Y.Y** | **N** |

> Note: ± shows standard deviation; **bold** = best result

#### Result Analysis
[Detailed analysis of main experiment results]

### Ablation Study

#### Study Design
[Design rationale for the ablation study]

#### Ablation Results and Analysis

### Experiment Result Figures
[Insert experiment result figures from the paper]

![Experiment results|800](images/experiment_results.pdf)

> Figure 2: [figure description]
**Note**: image filename must match actual file (arXiv images are usually `.pdf`)

## In-Depth Analysis

### Research Value Assessment

#### Theoretical Contributions
- **Contribution 1**: [detailed description]
  - Innovation: [type of innovation/new method/new perspective]
  - Academic value: [value to the research community]
  - Scope of impact: [affected research areas]

- **Contribution 2**: [detailed description]
  [similar format]

#### Practical Application Value
- **Use case 1**: [scenario description]
  - Applicability: [how well the method fits this scenario]
  - Advantage: [advantage over existing solutions]
  - Potential impact: [likely impact]

- **Use case 2**: [scenario description]
  [similar format]

#### Domain Impact
- **Short-term**: [near-term likely impact]
- **Mid-term**: [mid-term likely impact]
- **Long-term**: [long-term likely impact]
- **Potential shift**: [possible paradigm changes]

### Method Advantages

#### Advantage 1: [Advantage Name]
- **Description**: [detailed description]
- **Technical basis**: [technical foundation of this advantage]
- **Experimental validation**: [how experiments validate this]
- **Comparative analysis**: [how much better than existing methods]

#### Advantage 2: [Advantage Name]
[similar format]

#### Advantage 3: [Advantage Name]
[similar format]

### Limitations Analysis

#### Limitation 1: [Limitation Name]
- **Description**: [detailed description]
- **Manifestation**: [how it appears in practice]
- **Root cause**: [fundamental cause]
- **Impact**: [effect on practical applications]
- **Possible solutions**: [how to mitigate or resolve]

#### Limitation 2: [Limitation Name]
[similar format]

### Applicability

#### Suitable Scenarios
- **Scenario 1**: [scenario description]
  - Why it fits: [reason]
  - Expected effect: [what to expect]
  - Notes: [things to watch out for]

- **Scenario 2**: [scenario description]
  [similar format]

#### Unsuitable Scenarios
- **Scenario 1**: [scenario description]
  - Why it doesn't fit: [reason]
  - Alternative: [suggested alternative]

- **Scenario 2**: [scenario description]
  [similar format]

## Comparison with Related Papers

### Selection Criteria
[Why these papers were chosen for comparison]

### [[Related Paper 1]] - [Paper Title]

#### Basic Info
- **Authors**: [authors]
- **Published**: [date]
- **Venue**: [venue]
- **Core method**: [one-sentence summary]

#### Method Comparison
| Dimension | Related Paper 1 | This Paper |
|-----------|----------------|------------|
| Core idea | [description] | [description] |
| Technical approach | [description] | [description] |
| Key components | [description] | [description] |
| Innovation level | [description] | [description] |

#### Performance Comparison
| Dataset | Metric | Related Paper 1 | This Paper | Improvement |
|---------|--------|----------------|------------|-------------|
| Dataset1 | Metric1 | X.X | Y.Y | +Z.Z% |
| Dataset2 | Metric2 | X.X | Y.Y | +Z.Z% |

#### Relationship Analysis
- **Relationship type**: [improves / extends / compares / follows]
- **Improvements**: [what this paper improves over that one]
- **Advantages**: [advantages of this paper's method]
- **Disadvantages**: [disadvantages of this paper's method]
- **Complementarity**: [whether the two methods complement each other]

### [[Related Paper 2]] - [Paper Title]
[similar format]

### [[Related Paper 3]] - [Paper Title]
[similar format]

### Comparison Summary
[Summary of all comparison papers]

## Technical Lineage

### Research Thread
This paper belongs to [research thread name]. Core characteristics of this thread:
- Characteristic 1: [description]
- Characteristic 2: [description]
- Characteristic 3: [description]

### Development History

[Milestone 1] → [Milestone 2] → [Milestone 3] → [This paper] → [Future direction] ↑ ↑ ↑ ↑ [Paper A] [Paper B] [Paper C] [This paper]

### Position in the Research Thread
- **Building on**: [what prior work this inherits from]
- **Enabling**: [what foundation this provides for future work]
- **Key node**: [why this is a key node in the thread]

### Specific Sub-direction
This paper focuses on [specific sub-direction]. Key research focuses:
- Focus 1: [description]
- Focus 2: [description]

### Related Work Map
[Use text or diagrams to show relationships with related work]

## Future Work

### Author-Suggested Future Work
1. **Suggestion 1**: [author's suggestion]
   - Feasibility: [whether feasible]
   - Value: [potential value]
   - Difficulty: [implementation difficulty]

2. **Suggestion 2**: [author's suggestion]
   [similar format]

### Analysis-Based Future Directions
1. **Direction 1**: [direction description]
   - Motivation: [why this direction is worth researching]
   - Possible approaches: [possible research methods]
   - Expected outcomes: [likely results]
   - Challenges: [challenges to face]

2. **Direction 2**: [direction description]
   [similar format]

3. **Direction 3**: [direction description]
   [similar format]

### Improvement Suggestions
[Specific improvement suggestions for the method in this paper]
1. **Improvement 1**: [improvement description]
   - Current problem: [existing issue]
   - Improvement approach: [how to improve]
   - Expected effect: [expected outcome]

2. **Improvement 2**: [improvement description]
   [similar format]

## My Overall Assessment

### Value Score

#### Overall Score
**[X.X]/10** — [brief rationale]

#### Dimension Scores

| Dimension | Score | Rationale |
|-----------|-------|-----------|
| Innovation | [X]/10 | [detailed rationale] |
| Technical quality | [X]/10 | [detailed rationale] |
| Experimental completeness | [X]/10 | [detailed rationale] |
| Writing quality | [X]/10 | [detailed rationale] |
| Practicality | [X]/10 | [detailed rationale] |

### Key Points

#### Technical points worth attention

#### Parts needing deeper understanding

## My Notes

%% User can add personal reading notes here %%

## Related Papers

### Directly Related
- [[Related Paper 1]] — [relationship: improves/extends/compares/etc.]
- [[Related Paper 2]] — [relationship]

### Background Related
- [[Background Paper 1]] — [relationship]
- [[Background Paper 2]] — [relationship]

### Follow-up Work
- [[Follow-up Paper 1]] — [relationship]
- [[Follow-up Paper 2]] — [relationship]

## External Resources
[List links to related videos, blog posts, projects, etc.]

> [!tip] Key Insight
> [Most important insight from the paper, summarized in one sentence]

> [!warning] Notes
> - [Note 1]
> - [Note 2]
> - [Note 3]

> [!success] Recommendation
> ⭐⭐⭐⭐⭐ [Recommendation level and brief reason, e.g.: Strongly recommended! This is a landmark paper in XX field]

Step 5: Update Knowledge Graph

5.1 Add or Update Node

1. Read graph data

   • File path: $OBSIDIAN_VAULT_PATH/20_Research/PaperGraph/graph_data.json

2. Add or update node for this paper

   • Include analysis metadata:
     • quality_score
     • tags
     • domain
     • analyzed: true

3. Create edges to related papers

   • For each related paper, create an edge
   • Edge types:
     • improves: improvement relationship
     • related: general relationship
   • Weight: based on similarity (0.3–0.8)

4. Update timestamp

   • Set last_updated to current date

5. Save graph

   • Write updated graph_data.json

python "scripts/update_graph.py" \
  --paper-id "[PAPER_ID]" \
  --title "[paper title]" \
  --domain "[domain]" \
  --score [score]

Step 6: Display Analysis Summary

6.1 Output Format

## Paper Analysis Complete!

**Paper**: [[Paper Title]] (arXiv:XXXX.XXXXX)

**Status**: ✅ Detailed note generated
**Note location**: [[20_Research/Papers/domain/YYYY-MM-DD-arXiv-ID.md]]

---

**Overall score**: [X.X/10]

**Dimension scores**:
- Innovation: [X/10]
- Technical quality: [X/10]
- Experimental completeness: [X/10]
- Writing quality: [X/10]
- Practicality: [X/10]

**Highlights**:
- [highlight 1]
- [highlight 2]
- [highlight 3]

**Main advantages**:
- [advantage 1]
- [advantage 2]

**Main limitations**:
- [limitation 1]
- [limitation 2]

**Related papers** (N):
- [[Related Paper 1]] — [relationship]
- [[Related Paper 2]] — [relationship]
- [[Related Paper 3]] — [relationship]

**Research thread**:
This paper belongs to [research thread], focusing on [sub-direction].

---

**Quick actions**:
- Click the note link to view detailed analysis
- Use `/paper-search` to find more related papers
- Open Graph View to see paper relationships
- Based on the analysis, decide whether to study in depth or skip

**Suggestions**:
- [specific suggestion 1 based on analysis]
- [specific suggestion 2 based on analysis]

Important Rules

• Preserve existing user notes — do not overwrite manually written notes
• Use comprehensive analysis — cover methodology, experiments, and value assessment
• Write content in English — translate and explain in English
• Cite related work — build connections to the existing knowledge base
• Objective scoring — use consistent scoring criteria
• Update knowledge graph — maintain relationships between papers
• Figure-rich — use all figures from the paper (architecture diagrams, method figures, experiment charts, etc.)
• Handle errors gracefully — if one source fails, continue with others
• Manage token use — be comprehensive but stay within token limits

Scoring Criteria

Score Details (0–10 scale)

Innovation:

• 9–10: novel breakthrough, new paradigm
• 7–8: significant improvement or combination
• 5–6: minor contribution, already known or established
• 3–4: incremental improvement
• 1–2: known or established

Technical quality:

• 9–10: rigorous methodology, well-reasoned approach
• 7–8: good approach, minor issues
• 5–6: acceptable approach, some problems
• 3–4: problematic approach
• 1–2: poor approach

Experimental completeness:

• 9–10: comprehensive experiments, strong baselines
• 7–8: good experiments, adequate baselines
• 5–6: acceptable experiments, partial baselines
• 3–4: limited experiments, weak baselines
• 1–2: poor or no baselines

Writing quality:

• 9–10: clear, well-organized
• 7–8: mostly clear, minor issues
• 5–6: understandable, partially unclear
• 3–4: hard to follow, confusing
• 1–2: poor writing

Practicality:

• 9–10: high practical impact, directly applicable
• 7–8: good practical potential
• 5–6: moderate practical value
• 3–4: limited practicality, theoretical only
• 1–2: low practicality

Relationship Type Definitions

• improves: clear improvement over related work
• extends: extends or builds on related work
• compares: direct comparison, may be better/worse in some aspects
• follows: follow-up work in the same research thread
• cites: citation (if citation data is available)
• related: general conceptual relationship

Error Handling

• Paper not found: check ID format, suggest searching
• arXiv down: use cache or retry later, note limitations in output
• PDF parse failure: fall back to abstract, note limitations
• Related papers not found: note lack of context
• Graph update failure: continue but skip graph update

Usage

When the user calls /paper-analyze [paper ID]:

Quick Execution (Recommended)

Use this bash script to run the full flow in one step:

#!/bin/bash

# Set variables
PAPER_ID="$1"
TITLE="${2:-TBD}"
AUTHORS="${3:-Unknown}"
DOMAIN="${4:-Other}"

# Run full flow
python "scripts/generate_note.py" \
  --paper-id "$PAPER_ID" \
  --title "$TITLE" \
  --authors "$AUTHORS" \
  --domain "$DOMAIN" || \
    echo "Note generation script failed"

# Extract images (call extract-paper-images skill)
# /extract-paper-images "$PAPER_ID" "$DOMAIN" "$TITLE"

Manual Step-by-Step (for Debugging)

Step 0: Initialize environment

mkdir -p /tmp/paper_analysis
cd /tmp/paper_analysis

Step 1: Identify paper

find "${VAULT_ROOT}/20_Research/Papers" -name "*${PAPER_ID}*" -type f

Step 2: Fetch paper content

# Download PDF and source (see Steps 2.1, 2.2, 2.3)

Step 3: Copy figures

/extract-paper-images "$PAPER_ID" "$DOMAIN" "$TITLE"

Step 4: Generate note

python "scripts/generate_note.py" \
  --paper-id "$PAPER_ID" \
  --title "$TITLE" \
  --authors "$AUTHORS" \
  --domain "$DOMAIN"

Step 5: Update graph

python "scripts/update_graph.py" \
  --paper-id "$PAPER_ID" \
  --title "$TITLE" \
  --domain "$DOMAIN" \
  --score 8.8

Notes

1. Frontmatter format (important): all string values must be in double quotes

   ---
   date: "YYYY-MM-DD"
   paper_id: "arXiv:XXXX.XXXXX"
   title: "Paper Title"
   authors: "Author List"
   domain: "[domain name]"
   quality_score: "[X.X]/10"
   created: "YYYY-MM-DD"
   updated: "YYYY-MM-DD"
   status: analyzed
   ---
   

   Obsidian requires strict YAML format — missing quotes will break frontmatter display!

2. Image paths: use relative paths images/xxx (no extension needed; Obsidian auto-detects)

   • Important: images extracted from arXiv are usually .pdf format; Obsidian can display PDF images directly
   • Use actual filenames, e.g. images/loss_curve.pdf or images/figure1.png

3. Wikilinks: use [[Paper Title]] format

4. Domain inference: automatically infer from paper content

5. Related papers: reference [[Related Paper]] in notes; the graph will auto-create edges

Key Features

Figure-rich: use all figures from the paper

• Save to correct location: 20_Research/Papers/[domain]/[paper-title]/images/
• Image index: generate images/index.md indexing all figures
• Difference from start-my-day: paper-analyze is for deep analysis of a single paper
• Comprehensive analysis: cover all sections, figure-rich