Virtual Reading Group

Orchestrate a multi-agent virtual academic reading group. Use when reading multiple papers, generating expert discussion notes, cross-examining positions acr...

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:

1. Research question — the lens through which papers are analyzed
2. Paper list — paths to PDFs/text files, or paper descriptions for web lookup
3. 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

📄 Full prompt template: See references/workflow.md → Phase 1

Wait for all Phase 1 agents to complete before proceeding.

4. Execute Phase 2 — Junior Discussion (Single Agent)

Spawn single agent with:

• Label: junior-discussion
• Model: opus (required — needs strong reasoning)
• Core instructions:
  • Read all Phase 1 outputs (notes + summaries)
  • For each paper: summarize claims, pose challenging questions to each expert
  • Generate Grand Questions: 3 unsolved problems, 2 testable hypotheses, 2 methodological gaps
  • Reference specific passages — be intellectually provocative

📄 Full prompt template: See references/workflow.md → Phase 2

Wait for Phase 2 to complete before proceeding.

5. Execute Phase 3 — Expert Responses (Parallel)

For each expert, spawn a sub-agent with:

• Label: expert-response-{expert_name}
• Model: opus (recommended)
• Core instructions:
  • 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 — disagree where warranted

📄 Full prompt template: See references/workflow.md → Phase 3

Wait for all Phase 3 agents to complete before proceeding.

6. Execute Phase 4 — Synthesis (Single Agent)

Spawn single agent with:

• Label: synthesis
• Model: opus (required — 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, §Section)
  • Include: Points of Consensus, Points of Disagreement, Open Questions
  • Synthesize, don't summarize — find the intellectual threads

📄 Full prompt template: See references/workflow.md → 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:

1. Spawn new expert response agent(s) with targeted follow-up questions
2. Re-run Phase 4 synthesis including the additional responses

Second Round

For a full second round (new questions, new responses):

1. Rename Phase 2-4 outputs with round suffix (e.g., Chen_discussion_r1.md)
2. Re-run Phase 2 with instruction to build on previous round
3. Continue through Phases 3-4

Recovery from Partial Run

If a phase fails:

1. Check error handling in references/workflow.md
2. Retry failed agent(s) individually
3. 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	—

Citation Requirements

Enforce in all agent prompts:

1. Every factual claim must reference a paper
2. Use format: (AuthorYear, §Section) or (AuthorYear, p.X)
3. Direct quotes must include section/page
4. Discussion claims must attribute speaker: [Expert_A], [Expert_B], [Junior]

⚠️ 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 — 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 📭 未讀
• 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" → 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 — reading is less reasoning-intensive
Budget	All phases use sonnet	Medium	$	Quick exploration, simpler papers

Recommendations:

• Phase 2 (Junior Discussion) benefits most from opus — requires synthesizing multiple papers and generating non-obvious questions
• Phase 4 (Synthesis) also benefits from opus — 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 — Detailed phase specifications and full prompt templates
• references/default-personas.md — Ready-to-use expert and junior researcher personas
• references/paper-notes-template.md — Template for individual paper notes

Assets

• assets/synthesis-template.md — Structure for the final integrated discussion summary