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PROMPT SPACE
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Freedata-analysisUniversal

deep-research-team

Deploy a hierarchical team of AI agents to perform 15-30 minute deep-dive research with parallel execution.

skill install https://www.promptspace.in/skills/deep-research-team

Advanced Multi-Agent Research Orchestration

The Deep Research Team skill transforms your AI environment into a high-powered research laboratory. Instead of relying on a single prompt that may hallucinate or miss nuances, this skill deploys a structured team of specialized agents that work in parallel to investigate, cross-validate, and synthesize complex information into publication-ready reports.

What It Does

The skill automates a multi-phase research workflow designed for depth and accuracy:

  • Phase 0 (Planning): A PM Agent designs a custom research architecture and directory structure based on your specific topic.
  • Phase A (Parallel Research): Multiple specialized agents are dispatched simultaneously to investigate different facets of the topic, ensuring high throughput and diverse perspectives.
  • Phase B & C (Synthesis & Audit): A Synthesis Agent identifies patterns and contradictions across findings, while a Review Agent (powered by high-reasoning models) performs a critical quality audit.
  • Phase D (Delivery): A Final Report Agent assembles a comprehensive document with a separate executive summary and full source citations.

Why Use This Skill?

Standard AI prompting often lacks the breadth needed for professional due diligence. This skill is superior because it uses a file-system relay for agent communication, preventing the "context drift" associated with long chat threads. It supports both a Quick Mode for rapid overviews and a Full Mode for academic-grade investigations, complete with automated quality scoring and error-checking.

Output & Capabilities

The result is a structured workspace containing individual module findings, a synthesis report, and a final executive summary. Every fact is mapped to its primary source, making it ideal for competitive analysis, investment research, and technical due diligence.

Use cases

  • Investment due diligence research
  • Competitive intelligence analysis
  • Technology evaluation and comparison
  • Market entry research

Example

Prompt

Run a full deep research report on the current state of solid-state battery commercialization.

Output

Quick Mode output structure:
ai_agent_ecosystem_2025/
├── 00_planning/research_plan.md
├── 01_platforms/research.md + sources.md (27 sources)
├── 02_technology/research.md + sources.md (30 sources)
├── 03_business/research.md + sources.md (25 sources)
└── final_report/executive_summary.md

Each research.md contains structured findings with data tables, key conclusions, and cross-references. Each sources.md logs every cited source with URL, type, confidence rating, and citation location. The executive summary synthesizes all modules into a single overview with key metrics, core findings, cross-module consensus/contradictions, and action recommendations.

Known limitations

Full Mode (6-13 agents) consumes significant API usage. Quick Mode (4 agents) is recommended for initial exploration. Research quality depends on web search availability. Reports default to English but support any language.

Frequently asked questions

Unlike standard AI chats that work linearly, this skill uses a hierarchical team of agents that research different sub-topics in parallel. This prevents 'context drift' and ensures a much higher level of detail, cross-validation, and source-checked accuracy than a single LLM could provide alone.