workshop

Coordinate parallel researcher agents to interrogate NotebookLM notebooks and synthesize evidence-backed findings.

Updated Feb 28, 2026

Search Tags:#multi-agent #notebooklm #prompting #research synthesis #parallel investigation #evidence gathering #cross-pollination

Ready to run

npx skills add https://github.com/cosmicdreams/claude-plugins --skill workshop-cosmicdreams

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: workshop
Source: https://github.com/cosmicdreams/claude-plugins/tree/main/research-lab/skills/workshop
Command: npx skills add https://github.com/cosmicdreams/claude-plugins --skill workshop-cosmicdreams

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Workshop reduces the time it takes to interrogate a NotebookLM notebook by running multiple facet-focused researcher agents in parallel, then synthesizing their findings into a coherent report.

Core Features & Use Cases

Parallel facet research: Splits investigation into 3–5 non-overlapping, answerable facets and assigns each to a dedicated researcher agent.
Cross-pollination protocol: Researchers exchange signal-only findings that intersect, contradict, or change priorities, improving overall synthesis quality.
Notebook querying workflow: Uses the notebook ask script with 5–8 focused questions per facet and records question/answer/source-level evidence.
Output-ready artifacts: Writes per-facet findings to engagement files (03-workshop-N.md) and produces a consolidated synthesis (03-workshop.md).

Quick Start

Give the AI the instruction "Swarm this notebook" along with the Notebook ID and 3–5 research facets, and it will return a synthesized workshop report.

Frequently Asked Questions about workshop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run parallel research on a NotebookLM notebook?▼

Multi-agent parallel interrogation splits your research into 3–5 non-overlapping facets, assigning each to a dedicated researcher agent. This reduces investigation time by querying NotebookLM sources simultaneously rather than sequentially.

How does cross-pollination work in multi-agent research synthesis?▼

Cross-pollination allows focused researchers to exchange signal-only findings that intersect, contradict, or change priorities. This message exchange protocol improves overall synthesis quality by sharing intersecting evidence across parallel facets.

Can I use multi-agent prompting for solo research investigation?▼

Yes, you can use multi-agent prompting for a PI-driven solo pass. The system applies the same parallel interrogation, evidence gathering, and cross-pollination protocols to derive findings from existing NotebookLM sources without multiple human researchers.

How many research questions does each focused agent ask per facet?▼

Each focused researcher agent asks 5–8 focused questions per facet. The notebook querying workflow uses a notebook ask script and records question, answer, and source-level evidence to ensure findings are backed by notebook sources.

What is the best way to synthesize evidence from multiple research facets?▼

The best way to synthesize evidence from multiple research facets is through multi-agent parallel investigation with a cross-pollination protocol. Dedicated researchers gather evidence per facet, exchange intersecting signals, and consolidate outputs into a single synthesis report.

What are the limitations of multi-agent notebook research?▼

A limitation of multi-agent notebook research is that facets must be non-overlapping and answerable from existing notebook sources. If the NotebookLM notebook lacks sufficient sources for a specific facet, the researcher cannot derive evidence-backed findings.