virtual-reading-group
Orchestrate a multi-agent virtual academic reading group. Use when reading multiple papers, generating expert discussion notes, cross-examining positions across papers, and synthesizing integrated summaries with full citations. Triggers on requests to analyze academic literature, run paper discussions, create reading group sessions, or synthesize research across multiple sources. Supports 1-50 papers with configurable expert personas (1-4 papers work but produce simpler single-expert output).
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/isonaei/virtual-reading-groupVirtual Reading Group
Orchestrate parallel expert agents to read papers, discuss findings, challenge each other's interpretations, and synthesize an integrated discussion document with traceable citations.
Quick Start
Minimum inputs required:
- Research question — the lens through which papers are analyzed
- Paper list — paths to PDFs/text files, or paper descriptions for web lookup
- Output directory — where all outputs are written
Optional inputs:
- Custom expert personas (default: see
references/default-personas.md) - Custom junior researcher persona
- Language preference (default: English)
- Number of experts (default: auto-calculated from paper count)
Workflow Overview
The skill runs 4 sequential phases. Each phase must complete before the next begins.
| Phase | Agents | Input | Output |
|---|---|---|---|
| 1. Paper Reading | N experts (parallel) | Papers + research question | {AuthorYear}_notes.md, {Expert}_session_summary.md |
| 2. Junior Discussion | 1 junior researcher | All Phase 1 outputs | {Junior}_discussion.md |
| 3. Expert Responses | N experts (parallel) | Phase 2 output + other experts' summaries | {Expert}_response_to_{Junior}.md |
| 4. Synthesis | 1 synthesizer | All previous outputs | Integrated_Discussion_Summary.md |
For detailed prompts and phase specifications: Read references/workflow.md.
Orchestration Procedure
⚠️ Important: The prompts below are abbreviated summaries. For full prompt templates that produce quality output, use
references/workflow.md. The pseudocode blocks show orchestration structure — adapt to your actual sub-agent spawning mechanism.
1. Validate Inputs
- Confirm research question is specified
- Confirm paper list is non-empty
- Confirm output directory exists or create it
- Load personas from user input or references/default-personas.md
2. Calculate Expert Assignment
Determine number of experts and paper batches:
if paper_count <= 4:
num_experts = 1
elif paper_count <= 10:
num_experts = 2
elif paper_count <= 20:
num_experts = min(4, ceil(paper_count / 5))
else:
num_experts = min(8, ceil(paper_count / 5))
Distribute papers evenly across experts (max 5 per expert).
# ⚠️ Context contamination warning: assigning >5 papers per expert degrades
# note quality — later papers in the batch get shallower treatment as context
# fills up. Prefer 3-5 papers per agent for best results.
3. Execute Phase 1 — Paper Reading (Parallel)
For each expert, spawn a sub-agent with:
- Label:
expert-reader-{expert_name} - Model: opus (or sonnet for budget)
- Core instructions:
- Read assigned papers through research question lens
- Write notes using
references/paper-notes-template.md - Save as
{output_dir}/{AuthorYear}_notes.md - Write session summary with cross-cutting themes
- Critical: Quote specific passages with section labels — all claims must be traceable
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-isonaei-virtual-reading-group": {
"enabled": true,
"auto_update": true
}
}
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