Paper To Agent GuideSAFE

🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: paper-to-agent-guide
description: "Transform research papers into interactive AI agents for exploration"
version: 1.0.0
author: wentor-community
source: https://github.com/paper2agent/Paper2Agent
metadata:
  openclaw:
    category: "research"
    subcategory: "automation"
    emoji: "📄"
    keywords:
      - paper-parsing
      - agent-generation
      - interactive-papers
      - research-automation
      - knowledge-extraction
---

# Paper-to-Agent Guide

A skill for transforming published research papers into interactive AI agents that can answer questions, explain methodology, and help replicate findings. Based on Paper2Agent (2K stars), this skill guides the agent through extracting structured knowledge from academic papers and creating conversational interfaces for deep exploration.

## Overview

Traditional paper reading is linear and passive. Paper-to-Agent converts this into an active, queryable experience. By parsing a paper's structure, extracting key claims, methodology details, and results, the agent becomes an expert on that specific paper, ready to answer follow-up questions, explain complex sections, and connect findings to the broader literature.

This approach is especially valuable for interdisciplinary researchers who need to quickly understand papers outside their primary expertise, for journal clubs seeking deeper discussion, and for students learning to critically evaluate published research.

## Paper Parsing Workflow

The agent should follow this structured workflow when converting a paper to an interactive agent:

**Step 1: Structure Extraction**
- Identify the paper's sections (abstract, introduction, methods, results, discussion, references)
- Extract the title, authors, affiliations, and publication venue
- Identify figure and table captions along with their referenced locations
- Note supplementary materials and their availability
- Detect the paper type (empirical, theoretical, review, meta-analysis)

**Step 2: Claim Extraction**
- Identify the primary research question or hypothesis
- Extract all major claims made in the paper
- Map each claim to its supporting evidence (data, citations, arguments)
- Note the strength of evidence for each claim (strong, moderate, suggestive)
- Identify limitations acknowledged by the authors

**Step 3: Methodology Mapping**
- Document the complete experimental or analytical pipeline
- Extract parameter values, dataset descriptions, and evaluation metrics
- Identify software tools and libraries used
- Note any preprocessing or data cleaning steps
- Map the methodology to established frameworks in the field

## Interactive Exploration Capabilities

Once a paper has been parsed, the agent can support these interaction patterns:

**Question-Answering**
- Answer specific questions about the paper's content with source references
- Explain technical terms in context of how the paper uses them
- Compare the paper's approach to common alternatives
- Identify what the paper does and does not address
- Generate summaries at different levels of detail (tweet-length, abstract, detailed)

**Critical Analysis**
- Evaluate the validity of statistical analyses
- Identify potential confounds not addressed by the authors
- Assess whether conclusions follow from the presented evidence
- Compare results to related work in the field
- Suggest follow-up experiments that would strengthen the findings

**Replication Assistance**
- Generate step-by-step replication guides from the methods section
- Identify missing details needed for exact replication
- Suggest parameter ranges for robustness checks
- Create data collection templates based on the paper's design
- List required resources (compute, data, equipment) for replication

## Knowledge Graph Construction

The skill supports building knowledge graphs from processed papers:

- Extract entities (methods, datasets, metrics, tools, concepts)
- Map relationships between entities (uses, extends, contradicts, supports)
- Link to external knowledge bases (OpenAlex, CrossRef, DOI)
- Track citation chains for key claims
- Identify research lineages and methodological evolution

## Multi-Paper Analysis

When multiple papers have been processed, the agent can:

- Compare methodologies across papers addressing similar questions
- Identify consensus findings and areas of disagreement
- Trace the evolution of a research direction over time
- Build synthesis summaries combining evidence from multiple sources
- Detect gaps in the literature that no existing paper addresses

## Integration with Research-Claw

This skill connects with other Research-Claw capabilities:

- Use literature search skills to find papers for processing
- Feed extracted knowledge into writing skills for literature reviews
- Connect methodology details to analysis skills for replication
- Store parsed papers in the local knowledge base for future reference
- Generate citation entries compatible with reference management tools

## Practical Tips

- Start with the abstract and conclusion to determine if full parsing is worthwhile
- Focus deep extraction on methods and results sections for empirical papers
- For theoretical papers, prioritize definitions, theorems, and proof sketches
- Always verify extracted claims against the original text before presenting them
- Flag areas where the paper's writing is ambiguous or inconsistent
- Use the parsed representation to generate discussion questions for journal clubs

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.