Deep Research

Conduct deep multi-phase research using parallel subagents and iterative search. Use for deep research requests, comprehensive analysis, competitive intelligence, market research, or thorough investigation of complex topics.

Deep Research Skill

Overview

This skill conducts thorough, multi-phase research using parallel subagents and iterative search methodology. It simulates ChatGPT Deep Research and Anthropic Deep Search by breaking complex topics into sub-questions, distributing work across 6-10 parallel research agents, and synthesizing findings into a structured report.

When to Use

Use this skill when the user requests:

• Deep research on a topic
• Comprehensive analysis
• Competitive intelligence
• Market research
• Thorough investigation (not quick facts)
• Multi-angle exploration of complex subjects

Research Methodology

Core Principles

1. Multi-pass queries — Never one-and-done; iterate based on findings
2. Source triangulation — Verify claims across 3-5 independent sources
3. Primary source hunting — Find original studies, docs, not just blog posts
4. Contradiction spotting — Flag where sources disagree; don't hide uncertainty
5. Synthesis over summary — Connect dots, identify patterns, surface insights

Parallel Agent Architecture

For deep research, spawn 6-10 subagents to explore different angles simultaneously:

Research Lead (you)
├── Agent 1: Background & definitions
├── Agent 2: Market/industry landscape
├── Agent 3: Key players/competitors
├── Agent 4: Technology/trends
├── Agent 5: Challenges/risks
├── Agent 6: Opportunities/future outlook
├── Agent 7: Case studies/examples
├── Agent 8: Data/statistics
└── Agent 9-10: Specialized deep-dives (as needed)

Search Tool Strategy

Use web_search with different modes per phase:

Mode	Use Case
deep-reasoning	Initial exploration, complex queries
deep	Broad topic coverage, 20-30 results
neural	Semantic matching, finding relevant pages
fast	Quick fact-checks, specific lookups
instant	Verifying names, dates, basic facts

Use web_fetch to:

• Extract full article content from promising URLs
• Read primary sources, studies, documentation
• Get details that search snippets miss

Workflow

Phase 1: Scoping (5 min)

1. Clarify the topic — Ask user if the request is ambiguous
2. Identify sub-questions — Break the topic into 6-10 research angles
3. Define success — What does a good answer look like?

Example sub-question breakdown for "AI agent platforms":

• What are AI agent platforms and how do they work?
• What's the market size and growth trajectory?
• Who are the major players (established + startups)?
• What technologies power these platforms?
• What are the main use cases?
• What challenges/limitations exist?
• What's the competitive landscape?
• What trends are emerging?

Phase 2: Parallel Research (15-25 min)

Spawn subagents with sessions_spawn for each research angle:

# Example subagent spawn
sessions_spawn(
  task="Research [specific angle]. Use web_search with mode=deep-reasoning, 20-30 results. Fetch full content from 5-10 key sources. Return: key findings, statistics, quotes with sources, contradictions spotted.",
  runtime="subagent",
  mode="run"
)

Each subagent should:

• Use appropriate web_search mode for their angle
• Fetch 5-10 full articles with web_fetch
• Return structured findings with source citations
• Flag uncertainties or conflicting information

Phase 3: Synthesis (10-15 min)

As research lead, consolidate findings:

1. Aggregate results — Collect all subagent outputs
2. Identify patterns — What themes emerge across angles?
3. Spot contradictions — Where do sources disagree?
4. Fill gaps — Run targeted searches for missing pieces
5. Verify claims — Cross-check key statistics across sources

Phase 4: Report Writing (10 min)

Structure the final report as follows:

Output Format

# [Research Topic]

## Executive Brief

[150-250 words: The 3-5 most important takeaways. Lead with the answer. What should the reader know after finishing this report?]

---

## 1. Background & Context

[Foundational information, definitions, why this matters]

## 2. [Key Theme 1]

[Deep dive with supporting evidence]

## 3. [Key Theme 2]

[Deep dive with supporting evidence]

## 4. [Key Theme 3]

[Deep dive with supporting evidence]

## 5. Challenges & Risks

[What could go wrong, limitations, open questions]

## 6. Opportunities & Outlook

[Future trends, emerging developments, what to watch]

## Key Takeaways

- [Bulleted summary of 5-7 most important points]

---

## Sources

[Numbered list with full URLs, titles, and 1-line context for each source]

1. [Title](URL) — [Brief context: what this source contributed]
2. [Title](URL) — [Brief context]
...

Citation Guidelines

• In-text — Use numbered brackets: [1], [2-4], [5, 7]
• Sources section — Full URL, title, and 1-line context
• Minimum sources — 20-30 for deep research
• Quality over quantity — Prefer primary sources, industry reports, reputable publications

Tool Usage

web_search

# Broad exploration
web_search query="[topic]" type="deep-reasoning" count=30 freshness="year"

# Targeted lookup
web_search query="[specific fact]" type="fast" count=10

# Recent developments
web_search query="[topic]" type="neural" count=20 freshness="month"

web_fetch

# Extract full content
web_fetch url="https://example.com/article" extractMode="markdown" maxChars=5000

sessions_spawn (for parallel research)

# Spawn research subagent
sessions_spawn(
  task="Research [specific angle]. Search with mode=deep-reasoning, 25 results. Fetch 8-10 full articles. Return structured findings with citations.",
  runtime="subagent",
  mode="run"
)

Quality Checks

Before delivering the report, verify:

• Executive brief captures the 3-5 most important takeaways
• All major claims have 2+ source citations
• Contradictions/uncertainties are flagged, not hidden
• Sources section has 20-30 entries with full URLs
• Report answers the original question thoroughly
• No obvious gaps in coverage

Adaptation

For Quick Research (<10 min)

• Skip subagent spawning
• Run 3-5 targeted searches yourself
• Aim for 10-15 sources
• Condense report structure

For Ultra-Deep Research (60+ min)

• Spawn 10-15 subagents
• Include primary source documents, academic papers
• Add data tables, comparisons, timelines
• Include appendix with raw findings

Notes

• Context efficiency — Subagents run in isolated sessions; only their findings load into your context
• Parallelism — Spawn all subagents at once, then sessions_yield to wait for completion
• Iterative — If initial findings reveal new angles, spawn follow-up agents
• Time boxing — Set runTimeoutSeconds on subagents to prevent runaway research