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).
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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).
Virtual 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 โ the lens through which papers are analyzed
Paper list โ paths to PDFs/text files, or paper descriptions for web lookup
Output directory โ 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.
For detailed prompts and phase specifications: Read references/workflow.md.
Orchestration Procedure
โ ๏ธ 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 โ 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).
# โ ๏ธ Context contamination warning: assigning >5 papers per expert degrades
# note quality โ 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 โ 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 โ all claims must be traceable
๐ Full prompt template: See references/workflow.md โ Phase 1
Wait for all Phase 1 agents to complete before proceeding.
Read junior's discussion + other experts' summaries + own notes
Respond to each question directed at them (150-300 words per response)
Reference specific paper passages, engage with other expert's perspective
Respond to Grand Questions from their domain expertise
Be collegial but intellectually rigorous โ disagree where warranted
๐ Full prompt template: See references/workflow.md โ Phase 3
Wait for all Phase 3 agents to complete before proceeding.
6. Execute Phase 4 โ Synthesis (Single Agent)
Spawn single agent with:
Label:synthesis
Model: opus (required โ complex reasoning)
Core instructions:
Read ALL files from Phases 1-3
Follow assets/synthesis-template.md structure
Organize by THEME, not by paper or speaker
Every claim attributed: [Expert_A]/[Expert_B]/[Junior] + (PaperCode, ยงSection)
Include: Points of Consensus, Points of Disagreement, Open Questions
Synthesize, don't summarize โ find the intellectual threads
๐ Full prompt template: See references/workflow.md โ Phase 4
7. Report Completion
List all generated files and provide a brief summary of the discussion themes.
Iteration and Follow-up
Deeper Discussion
If user wants experts to expand on specific points:
Spawn new expert response agent(s) with targeted follow-up questions
Re-run Phase 4 synthesis including the additional responses
Second Round
For a full second round (new questions, new responses):
Rename Phase 2-4 outputs with round suffix (e.g., Chen_discussion_r1.md)
Re-run Phase 2 with instruction to build on previous round
Continue through Phases 3-4
Recovery from Partial Run
If a phase fails:
Check error handling in references/workflow.md
Retry failed agent(s) individually
Continue from last successful phase (outputs are saved incrementally)
File Naming Conventions
File Type
Pattern
Example
Paper notes
{FirstAuthorLastName}{Year}_notes.md
Chen2024_notes.md
Expert summary
{ExpertLastName}_session_summary.md
Lin_session_summary.md
Junior discussion
{JuniorLastName}_discussion.md
Chen_discussion.md
Expert response
{ExpertLastName}_response_to_{JuniorLastName}.md
Lin_response_to_Chen.md
Synthesis
Integrated_Discussion_Summary.md
โ
Citation Requirements
Enforce in all agent prompts:
Every factual claim must reference a paper
Use format: (AuthorYear, ยงSection) or (AuthorYear, p.X)
Direct quotes must include section/page
Discussion claims must attribute speaker: [Expert_A], [Expert_B], [Junior]
โ ๏ธ Anti-Fabrication Rule (Critical)
Never fabricate citations. If an agent cannot find the exact passage in the source text:
Leave the field blank or write <!-- source not found -->
Do NOT paraphrase and present it as a quote
Do NOT infer what the paper "probably says"
Fabricated citations are worse than missing citations โ they corrupt the knowledge base silently. Accuracy > Coverage.
No Source = No Notes
If a paper has no PDF or markdown source available:
Write a placeholder note with status ๐ญ ๆช่ฎ
Leave all content sections blank
Do NOT attempt to write notes from memory or web search results
Only write substantive notes when the actual source document is accessible.
Scaling Guidelines
Papers
Experts
Batches
Estimated Time
1-6
1
1
15-20 min
7-12
2
2
20-30 min
13-24
3-4
3-4
30-45 min
25-50
4-8
5-8
45-90 min
Customization
Custom Personas
Replace default personas by providing:
Expert A: Dr. [Name], [Role]. Background in [X].
Emphasizes [methodology/perspective]. Skeptical of [Y].
Tone: [collegial/rigorous/provocative].
Expert B: Dr. [Name], [Role]. Background in [X].
...
See references/default-personas.md for complete templates.
Language
Pass the language parameter when invoking the orchestration:
All agent prompts include Language: {language} instruction
Agents read papers and write outputs in the specified language
Default: English
Example: "Run the reading group in Japanese" โ adds Language: Japanese to all phase prompts.
Model Selection
Model choice significantly impacts output quality and cost:
Configuration
Phases
Quality
Cost
Use When
Full opus
All phases use opus
Highest
$$$
Publication-quality analysis, complex papers
Mixed
Phase 1: sonnet, Phases 2-4: opus
High
$$
Good balance โ reading is less reasoning-intensive
Budget
All phases use sonnet
Medium
$
Quick exploration, simpler papers
Recommendations:
Phase 2 (Junior Discussion) benefits most from opus โ requires synthesizing multiple papers and generating non-obvious questions
Phase 4 (Synthesis) also benefits from opus โ thematic organization requires complex reasoning
Phase 1 (Reading) can use sonnet if papers aren't highly technical
Phase 3 (Responses) can use sonnet if questions are straightforward
Integration
This skill is standalone but works well with paper collection workflows:
literature-manager or similar skills: Use to gather and organize papers first, then pass the collection to virtual-reading-group
PDF extraction tools: Pre-extract text from PDFs if agents have trouble reading them directly
References
references/workflow.md โ Detailed phase specifications and full prompt templates
references/default-personas.md โ Ready-to-use expert and junior researcher personas
references/paper-notes-template.md โ Template for individual paper notes
Assets
assets/synthesis-template.md โ Structure for the final integrated discussion summary
