Paper Research Agent

Autonomous multi-agent paper research system. When user wants to research a topic, find related papers, or analyze academic literature, use this skill to orc...

Security Scan

The skill largely matches its stated purpose (search arXiv, download PDFs, spawn sub-agents, produce reports) but has several operational and safety inconsistencies—runtime pip installs, unsafely broad parallel agent spawning, and undeclared/assumed dependencies—that merit review before use.

Purpose & Capability

The name/description align with the code and instructions: it searches arXiv, downloads PDFs, and coordinates per-paper analyses. However the skill assumes the presence of an external 'paper-reader' skill/tool at a hard-coded path (~/.openclaw/skills/paper-reader/read_paper.py) and suggests a launch_agents.py script that is not present in the bundle. Those undeclared dependencies are unexpected and weaken the declared self-contained purpose.

Instruction Scope

Runtime instructions perform network downloads and write PDFs and task files to the workspace (expected), but they also instruct the agent to 'spawn as many agents in parallel as possible' using sessions_spawn. That gives the skill broad discretion to create many sub-agents (resource exhaustion or wide action surface). The SKILL.md also runs subprocess commands and expects external tools; it references other skill paths and an absent launch script, giving ambiguous/incomplete guidance.

Install Mechanism

There is no formal install spec in the registry, but the bundled script auto-installs Python packages via pip at runtime (arxiv, requests, pdfplumber). Auto-pip-install is common but increases risk because it executes package installation from PyPI during execution rather than a reviewed install step. This is moderate risk (supply-chain / arbitrary code from PyPI) and should be considered when running in production.

Credentials

The skill requests no secrets or environment variables (good). It does, however, access and write files under the agent workspace and references other skills' paths (~/.openclaw/skills/paper-reader), which is not declared. That implies implicit reliance on other skill artifacts and file-system access that the description doesn't call out explicitly.

Persistence & Privilege

The skill is not forced-always and allows normal autonomous invocation. The main privilege concern is operational: instructions to spawn many parallel sub-agents can amplify the blast radius of any misbehaving sub-agent. There is no sign the skill modifies other skills' configs, but it does read/write workspace files and produce tasks for autonomous agents.

What to consider before installing

Before installing or running this skill: - Inspect scripts/research_pipeline.py and references/analysis_standards.md yourself to verify behavior and confirm there are no hidden network endpoints or unexpected commands. - Note it will pip-install packages at runtime (arxiv, requests, pdfplumber). If you need supply-chain assurance, pre-install vetted versions or run in an isolated environment. - The skill expects a paper-reader tool at ~/.openclaw/skills/paper-reader/read_paper.py and references a launch_agents.py that is not present; ensure those dependencies exist and are trustworthy. - Limit parallelism: do not launch 'as many agents as possible' on your machine—test with a small max_papers and controlled concurrency to avoid resource exhaustion or runaway agent spawning. - Run first in a sandbox or restricted environment (network and process limits) and review generated _agent_tasks.json and task files before actually invoking sub-agents. - If you lack the ability to audit the skill code, treat it as higher-risk and prefer manual execution of its components rather than fully autonomous runs.

Paper Research Agent - Autonomous Multi-Agent Research System

When to Use

Use this skill when the user wants to:

• Research papers on a specific topic
• Find related literature for a research area
• Analyze academic papers in depth
• Build a literature survey
• Identify research gaps
• Compare methods across papers

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)

• Parse user's research intent from natural language
• Execute vertical deep search or iterative exploration
• Generate research graph with papers at different levels

Phase 2: PDF Download (Automated)

• Download PDFs from arxiv
• Deduplicate and version management
• Standard naming: {paper_title}-{arxiv_id}.pdf

Phase 3: Parallel Agent Analysis (Automated - Key)

• Spawn multiple sub-agents (one per paper)
• Each agent reads full PDF using paper-reader
• Generate 6-section detailed analysis
• Agents run in parallel for speed

Phase 4: Report Integration (Automated)

• Collect all agent analyses
• Generate comparison tables
• Identify research gaps
• Output comprehensive survey

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:

• Detailed requirements for each of the 6 sections
• Possible sub-sections to consider (as hints, not rigid requirements)
• Quality checklists
• How to use paper-reader tool
• Report format template

Summary of 6 Required Sections

Section 1: Research Background

• Domain context and research lineage
• Key prior works cited (3-5 papers)
• Technical state when this paper was published
• Goal: Help user understand the research landscape

Section 2: Research Problem

• Specific problem being solved
• Limitations of existing methods (cite original text)
• Core assumptions and insights
• Goal: Clarify what problem the author identified

Section 3: Core Innovation

• Detailed method/system architecture
• Technical details (network structure, dimensions)
• Key formulas in LaTeX format
• Comparison table with prior methods
• Goal: Understand exactly what the author did

Section 4: Experimental Design

• Dataset details (name, scale, characteristics)
• Baseline methods used
• Evaluation metrics
• REAL experimental data tables (copy from paper)
• Ablation study results
• Goal: Extract real data for comparison

Section 5: Key Insights

• Core findings from experiments
• Domain insights (what works/doesn't work)
• Practical recommendations
• Goal: Learn actionable lessons

Section 6: Future Work

• Limitations acknowledged by authors
• Unsolved problems
• Potential research directions (at least 3)
• Goal: Identify research gaps for user's innovation

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

Quality Enforcement

Agents MUST:

• ✅ Read EVERY section of the PDF (not just abstract)
• ✅ Extract REAL tables with actual data
• ✅ Cite sources with exact locations [Section X.Y]
• ✅ Use paper-reader tool for extraction
• ❌ NEVER fabricate data
• ❌ NEVER skip sections

Usage

Agent Execution (When User Requests Research)

Trigger phrases:

• "帮我调研一下XXX的相关论文"
• "Research papers on X"
• "Find related literature about X"
• "分析XXX领域的论文"

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

• scripts/research_pipeline.py: Main orchestration script
• scripts/research_probe.py: Intelligent search module
• scripts/paper_downloader.py: PDF download module
• scripts/agent_task_generator.py: Sub-agent task generator

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:

• Minimum 3000 words
• At least 3 data tables
• At least 10 citations to original text
• All citations must include exact location [Section X.Y] or [Table N]
• No fabricated data - all numbers must come from the actual paper

Error Handling

If paper download fails:

• Skip and continue with available papers
• Log error in summary

If agent analysis fails:

• Retry once
• If still failing, mark as "analysis_failed" in summary
• Continue with other papers

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):

• arxiv
• requests
• pdfplumber (for paper-reader)

Notes

• Each paper analysis takes 5-10 minutes
• Parallel execution significantly speeds up research
• Always verify critical data points manually
• The system respects arxiv rate limits (3s delay between downloads)