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.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/autosolutionsai-didac/autosolutions-deep-researchDeep 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:
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-autosolutionsai-didac-autosolutions-deep-research": {
"enabled": true,
"auto_update": true
}
}
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