A multi-agent orchestration skill for reading batches of academic papers, conducting expert-level discussions, and generating integrated research summaries.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install virtual-reading-group
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install virtual-reading-group using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Virtual Reading Group?
The Virtual Reading Group is a powerful orchestration framework designed for Openclaw Skills that automates the deep analysis of complex academic literature. It moves beyond basic summarization by simulating a dialectical environment where multiple AI expert personas read, challenge, and respond to one another's interpretations of research papers. This process ensures a more rigorous and nuanced understanding of the source material than a single-pass analysis could provide.
By leveraging the multi-agent capabilities of Openclaw Skills, this tool handles the heavy lifting of cross-referencing claims across up to 50 papers simultaneously. It is specifically engineered to maintain intellectual integrity through strict citation requirements and anti-fabrication rules, ensuring that every synthesized insight is traceable back to the original source text.
Virtual Reading Group Use Cases
- Conducting large-scale literature reviews for academic research or technical white papers.
- Identifying methodological gaps and hidden contradictions across multiple research studies.
- Generating expert-level discussion notes and synthesis for graduate-level reading groups.
- Extracting testable hypotheses and unsolved problems from a specific niche of academic literature.
How Virtual Reading Group Works
- The system validates inputs including the research question, paper list, and output directory to ensure a clean orchestration environment.
- In Phase 1, parallel expert agents read assigned papers (max 5 per agent) to generate detailed notes and session summaries through the lens of the research question.
- In Phase 2, a junior researcher agent synthesizes the expert notes to identify common themes and pose challenging, provocative questions to the experts.
- In Phase 3, the expert agents return to provide rigorous responses to the junior researcher's challenges, engaging with other experts' perspectives.
- In Phase 4, a synthesis agent compiles all discussions into a final integrated document organized by theme, featuring full citations and attribution.
Virtual Reading Group Setup
To use this skill within Openclaw Skills, ensure your papers are accessible as PDFs or text files. You can customize the expert personas by editing the reference files.
# Example execution command structure
openclaw run virtual-reading-group \
--question "How does RAG impact LLM hallucination rates?" \
--papers "./research/pdfs/" \
--output "./results/rag-analysis/"
Virtual Reading Group Data Schema & Taxonomy
The skill organizes research data into a clear hierarchy of markdown files to maintain traceability across the orchestration lifecycle:
| File Category | Pattern | Purpose |
|---|---|---|
| Paper Analysis | {AuthorYear}_notes.md |
Primary notes extracted from individual source documents. |
| Agent Summaries | {Expert}_session_summary.md |
The high-level perspective of a specific expert persona. |
| Discussion Logic | {Junior}_discussion.md |
The synthesis of initial findings and critical questions. |
| Expert Rebuttals | {Expert}_response_to_{Junior}.md |
Refined arguments responding to specific critiques. |
| Final Output | Integrated_Discussion_Summary.md |
The final themed synthesis with full speaker attribution. |
Virtual Reading Group Advanced Features
- Context-Aware Expert Scaling: Automatically calculates the optimal number of agents (up to 8) to prevent context window degradation and ensure high-quality analysis.
- Multi-Model Optimization: Supports mixing models like Claude 3 Opus for complex synthesis and Sonnet for cost-effective parallel reading.
- Strict Anti-Fabrication Logic: Enforces a 'No Source = No Notes' policy, preventing the AI from hallucinating citations or inferring data not present in the provided papers.
- Persona Customization: Allows users to define custom expert backgrounds, tones, and methodological biases for more targeted research outcomes within Openclaw Skills.
- Iterative Round Support: Enables multiple rounds of discussion to drill deeper into specific intellectual threads identified during the initial synthesis.
SKILL.md
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