Quick Research

Quick Research

Overview

Quick research enables comprehensive topic exploration using a multi-agent architecture. A lead researcher (Claude Code) orchestrates multiple parallel research sub-agents to explore different aspects of a topic simultaneously, then synthesizes findings into a well-cited report.

This approach mirrors Anthropic's production research system which found that multi-agent systems outperform single-agent by 90%+ on breadth-first queries.

When to Use This Skill

• Complex research requiring multiple independent directions
• Comparative analyses (e.g., "Compare OpenAI vs Anthropic vs Google approaches to AI safety")
• List/ranking requests (e.g., "Find the top 20 AI companies in healthcare")
• Validation questions requiring deep domain exploration
• Any research exceeding what a single web search can accomplish

Architecture

# PSEUDO-CODE - Conceptual workflow, not executable code

def deep_research(user_query):
    # Phase 1: Scope
    brief = clarify_and_create_research_brief(user_query)

    # Phase 2: Research Loop (max 3 iterations)
    all_findings = []
    for iteration in range(3):
        subtopics = identify_gaps_or_subtopics(brief, all_findings)
        if not subtopics:
            break  # sufficient findings

        # Spawn parallel sub-agents (in single message)
        findings = parallel([
            Task(subagent_type="research-assistant", prompt=topic)
            for topic in subtopics
        ])
        all_findings.extend(findings)

    # Phase 3: Synthesize
    synthesized = merge_and_deduplicate(all_findings)

    # Phase 4: Report
    return generate_report_with_citations(brief, synthesized)

Quick Research Workflow

Phase 1: Scope the Research

Before spawning sub-agents, clarify the research scope:

1. Analyze the query - What specific information does the user need?
2. Ask clarifying questions if needed - Use AskUserQuestion for ambiguous terms, acronyms, or missing context
3. Create a research brief - A focused statement capturing:
   • The core research question
   • Specific dimensions to explore
   • Any constraints or preferences from the user
   • Source quality preferences (academic, official, etc.)

Phase 2: Delegate Research to Sub-Agents

Use the Task tool with subagent_type: "research-assistant" to spawn parallel research agents.

Scaling Rules

Delegation Best Practices

1. Provide complete, standalone instructions - Sub-agents cannot see other agents' work
2. Specify clear task boundaries - Avoid overlapping responsibilities
3. Define output format expectations - What structure should findings take?
4. Include source guidance - What types of sources to prioritize?
5. Avoid acronyms - Be explicit and specific in task descriptions

Example: Spawning Parallel Sub-Agents

For a query like "Compare OpenAI vs Anthropic vs Google approaches to AI safety":

Use Task tool THREE times in parallel (single message, multiple tool uses):

Task 1:
  subagent_type: "research-assistant"
  prompt: |
    Research OpenAI's approach to AI safety and alignment.
    Focus on:
    - Their philosophical framework for AI safety
    - Key research priorities and publications
    - Their stance on the alignment problem
    - Notable safety initiatives and teams

    Return findings with inline citations in format [Source Title](URL).
    Prioritize official OpenAI sources, research papers, and executive statements.

Task 2:
  subagent_type: "research-assistant"
  prompt: |
    Research Anthropic's approach to AI safety and alignment.
    Focus on:
    - Their philosophical framework (Constitutional AI, etc.)
    - Key research priorities and publications
    - Their stance on the alignment problem
    - Notable safety initiatives and teams

    Return findings with inline citations in format [Source Title](URL).
    Prioritize official Anthropic sources and research papers.

Task 3:
  subagent_type: "research-assistant"
  prompt: |
    Research Google DeepMind's approach to AI safety and alignment.
    Focus on:
    - Their philosophical framework for AI safety
    - Key research priorities and publications
    - Their stance on the alignment problem
    - Notable safety initiatives and teams

    Return findings with inline citations in format [Source Title](URL).
    Prioritize official DeepMind sources and research papers.

CRITICAL: Launch all sub-agents in a SINGLE message with multiple Task tool calls to enable true parallelization.

Phase 3: Synthesize Findings

After all sub-agents return:

1. Collect all findings - Gather results from each sub-agent
2. Identify patterns and gaps - What themes emerge? What's missing?
3. Spawn additional sub-agents if needed - Fill gaps with targeted follow-up research
4. Deduplicate and organize - Remove redundant information, structure by theme

Phase 4: Generate Final Report

Create a comprehensive report that:

1. Answers the research brief directly
2. Organizes by logical structure (see Report Structures below)
3. Includes all relevant findings with inline citations
4. Ends with Sources section listing all referenced URLs

Report Structures

For comparisons:

# [Topic] Comparison

## Overview
[Brief context]

## [Element A]
[Detailed findings]

## [Element B]
[Detailed findings]

## Comparative Analysis
[Cross-cutting comparison]

## Conclusion
[Key takeaways]

## Sources
[Numbered list of all sources]

For lists/rankings:

# Top [N] [Category]

## 1. [Item]
[Details with citations]

## 2. [Item]
[Details with citations]

...

## Sources
[Numbered list]

For topic exploration:

# [Topic] Research Report

## Overview
[Context and scope]

## [Aspect 1]
[Detailed findings]

## [Aspect 2]
[Detailed findings]

## Key Insights
[Synthesized conclusions]

## Sources
[Numbered list]

Citation Rules

• Assign each unique URL a single citation number
• Use inline citations: [1] or [Source Title](URL)
• Number sources sequentially (1, 2, 3...) without gaps
• End with ## Sources section listing all sources:

  ## Sources
  [1] Source Title: URL
  [2] Source Title: URL
  

Hard Limits

To prevent excessive resource usage:

• Maximum 10 parallel sub-agents per research iteration
• Maximum 3 research iterations (initial + 2 follow-ups)
• Stop when findings are sufficient - Don't pursue perfection
• Token awareness - Multi-agent systems use ~15x more tokens than chat

Key Insights from Anthropic's Research System

1. Token usage explains 80% of performance variance - Distribute work across agents with separate context windows
2. Start wide, then narrow - Broad queries first, progressively focus
3. Context isolation prevents failures - Each sub-agent handles one subtopic cleanly
4. Sub-agent output compression - Have sub-agents summarize their findings to avoid "game of telephone" information loss
5. Parallel execution cuts time 90% - Always spawn sub-agents in parallel when independent

References

For detailed prompt templates and architecture details, see:

• references/prompts.md - Research agent prompt templates
• references/architecture.md - Multi-agent system architecture details