Paper Research Agent - Autonomous Multi-Agent Research System

---

name: paper-research-agent

description: |

Autonomous multi-agent paper research system. When user wants to research a topic, find related papers,

or analyze academic literature, use this skill to orchestrate the full pipeline: intelligent search →

PDF download → parallel agent analysis → comprehensive report generation.

Triggers on: "research papers on X", "find related literature", "analyze papers",

"调研论文", "查找相关文献", "分析论文", "帮我调研XXX领域"

---

# Paper Research Agent - Autonomous Multi-Agent Research System

When to Use

Use this skill when the user wants to:

Core Workflow

The system autonomously executes the full research pipeline:

User Query → Research Probe → PDF Download → Parallel Agent Analysis → Integrated Report

Phase 1: Research Probe (Automated)

Phase 2: PDF Download (Automated)

Phase 3: Parallel Agent Analysis (Automated - Key)

Phase 4: Report Integration (Automated)

Agent Analysis Requirements

Each sub-agent MUST generate a 6-section report following the detailed standards in:

**`references/analysis_standards.md`**

SubAgent MUST read this reference file before starting analysis to understand:

Summary of 6 Required Sections

Section 1: Research Background

Section 2: Research Problem

Section 3: Core Innovation

Section 4: Experimental Design

Section 5: Key Insights

Section 6: Future Work

**For full details, sub-section hints, and quality standards - READ `references/analysis_standards.md`**

Quality Enforcement

Agents MUST:

Usage

Agent Execution (When User Requests Research)

**Trigger phrases**:

**Agent Action**:

Step 1: Execute main pipeline

import subprocess
result = subprocess.run([
    "python3", 
    "~/.openclaw/workspace/skills/paper-research-agent/scripts/research_pipeline.py",
    "--query", "{user_topic}",
    "--mode", "vertical",
    "--max-papers", "10",
    "--output", "./research_{topic}"
], capture_output=True, text=True)

print(result.stdout)

Step 2: Read generated agent tasks

import json
with open("./research_{topic}/_agent_tasks.json") as f:
    tasks = json.load(f)

Step 3: Spawn parallel sub-agents for analysis (CRITICAL)

# Spawn multiple agents in parallel for each paper
for task_info in tasks:
    sessions_spawn(
        agentId="main",
        mode="run", 
        runtime="subagent",
        task=task_info['task'],
        timeoutSeconds=600  # 10 minutes per paper
    )

**Important**: Launch as many agents in parallel as possible for speed.

Step 4: After all agents complete, integrate results

# Collect all analysis reports
# Generate integrated survey
# Present to user

Output Structure

research_output/
├── _research_summary.json              # Research metadata
├── probe/
│   ├── _probe_results.json            # Search results
│   └── _probe_report.md               # Human-readable probe report
├── papers/
│   ├── {title}-{arxiv_id}.pdf         # Downloaded PDFs
│   └── ...
├── analysis/
│   ├── {title}-{arxiv_id}_analysis.md # 6-section agent reports
│   └── ...
└── _integrated_survey.md              # Final integrated survey

Key Scripts

Report Format Standards

Each sub-agent analysis report MUST follow this exact 6-section structure:

# 📄 {Paper Title}

> **ArXiv ID**: {id}  
> **Authors**: {authors}  
> **Published**: {date}

---

## Section 1: Research Background
- Domain context
- Key prior works (3-5 papers with citations)
- Technical state at publication time
- Citations: [Section X.Y]

## Section 2: Research Problem
- SPECIFIC problem being solved
- SPECIFIC limitations of existing methods (quote original)
- Core assumptions
- Citations: [Section X.Y, "exact quote"]

## Section 3: Core Innovation
- Method/system architecture (detailed)
- Technical details (network structure, dimensions)
- Key formulas in LaTeX: $...$
- Comparison table:
  | Aspect | Prior Work | This Paper | Advantage |
  |--------|-----------|------------|-----------|
- What is genuinely new

## Section 4: Experimental Design
- Dataset: Name, size, characteristics
- Baseline methods: Specific names
- Metrics: Formulas, units
- Results table (REAL data):
  | Method | Metric1 | Metric2 |
  |--------|---------|---------|
  | This | X.XX | X.XX |
  | Baseline | X.XX | X.XX |
- Ablation study results

## Section 5: Key Insights
- Core findings from experiments
- What works/doesn't work
- Design choices and impact
- Practical recommendations

## Section 6: Future Work
- Limitations acknowledged by authors
- Unsolved problems
- Future directions (3+)

---

*Analysis by Paper Research Agent*  
*Date: {timestamp}*

**Quality Requirements**:

Error Handling

If paper download fails:

If agent analysis fails:

Best Practices

1. **For deep research**: Use `--mode vertical` (searches 4 levels deep)

2. **For exploration**: Use `--mode iterative` (progressive discovery)

3. **For specific paper**: Use `--mode horizontal` (find related work)

4. **Parallel agents**: System auto-spawns optimal number based on paper count

5. **Quality check**: Always verify a few random citations manually

Example Session

**User**: "帮我调研扩散策略在机器人操作中的应用"

**Agent**:

1. Executes research probe with query "扩散策略 机器人操作"

2. Finds 30 related papers across 4 levels

3. Downloads PDFs for top 10 papers

4. Spawns 10 sub-agents in parallel

5. Each agent analyzes one paper with 6-section format

6. Collects all analyses

7. Generates integrated survey with comparison tables

8. Presents final report to user

**Output**: Complete research package with all papers analyzed and integrated survey.

Dependencies

Required Python packages (auto-installed):

Notes