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
- Multi-pass queries — Never one-and-done; iterate based on findings
- Source triangulation — Verify claims across 3-5 independent sources
- Primary source hunting — Find original studies, docs, not just blog posts
- Contradiction spotting — Flag where sources disagree; don't hide uncertainty
- 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)
- Clarify the topic — Ask user if the request is ambiguous
- Identify sub-questions — Break the topic into 6-10 research angles
- 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:
- Aggregate results — Collect all subagent outputs
- Identify patterns — What themes emerge across angles?
- Spot contradictions — Where do sources disagree?
- Fill gaps — Run targeted searches for missing pieces
- 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:
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
