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deep-research
Conduct thorough, multi-source research on complex topics with structured findings and citations.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Conduct thorough, multi-source research on complex topics with structured findings and citations.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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| name | deep-research |
| description | Conduct thorough, multi-source research on complex topics with structured findings and citations. |
Conduct comprehensive, multi-source research on complex topics. Systematically gather, evaluate, and synthesize information into structured reports with proper citations.
This skill follows a tree-like exploration pattern inspired by leading open-source research tools:
The core pattern: decompose the question -> search broadly -> read deeply -> identify gaps -> refine queries -> synthesize with citations.
Before starting research, clearly define:
Decompose the topic into 5 distinct focus areas, then launch 5 research subagents in parallel using startAsyncSubagent. Each subagent gets a specific focus area and set of search terms, searches independently, and returns its findings with citations.
How to decompose: After the broad landscape search in Phase 1, identify 5 non-overlapping angles. For example, researching "state of electric vehicles 2026" might decompose into:
Launch all 5 in parallel:
// Launch 5 research subagents simultaneously
await startAsyncSubagent({
task: `Research FOCUS AREA 1: [Market & Competition]
Topic context: [brief description of the overall research question]
Your job: Search for information specifically about [focus area]. Run at least 3-4 webSearch queries with different angles:
- [specific search term 1]
- [specific search term 2]
- [specific search term 3]
- [specific search term 4]
For the most promising results, use webFetch to read the full article.
Return your findings as a structured summary with:
- Key facts and data points (with source URLs)
- Notable claims that need cross-referencing
- Gaps or unanswered questions
- At least 5 distinct sources with URLs`
});
// Repeat for focus areas 2-5 with their own tailored search terms
await startAsyncSubagent({ task: `Research FOCUS AREA 2: [Technology] ...` });
await startAsyncSubagent({ task: `Research FOCUS AREA 3: [Policy & Regulation] ...` });
await startAsyncSubagent({ task: `Research FOCUS AREA 4: [Infrastructure] ...` });
await startAsyncSubagent({ task: `Research FOCUS AREA 5: [Consumer & Economics] ...` });
// Wait for all subagents to complete
const results = await waitForBackgroundTasks();
Each subagent should:
webSearch queries with different phrasings and angleswebFetch on the 2-3 most relevant results to extract detailed dataThis approach gathers 25+ distinct sources across 5 focus areas simultaneously, producing far more comprehensive coverage than sequential searching.
After collecting all subagent results, proceed to Phase 3 to evaluate and cross-reference.
Assess each source for credibility:
Use webFetch to read full articles from the most promising search results.
Organize findings thematically (what separates deep research from simple search):
Structure the final report clearly:
# [Research Topic]
## Executive Summary
[2-3 paragraph overview of key findings and conclusions]
## Background
[Context needed to understand the topic]
## Key Findings
### Finding 1: [Theme]
[Detailed analysis with source citations]
### Finding 2: [Theme]
[Detailed analysis with source citations]
### Finding 3: [Theme]
[Detailed analysis with source citations]
## Analysis
[Cross-cutting analysis, patterns, implications]
## Limitations
[What couldn't be determined, data gaps, caveats]
## Recommendations
[Actionable next steps based on findings]
## Sources
[Numbered list of all sources with URLs]
// Phase 1: Broad landscape search to identify focus areas
const overview = await webSearch({ query: "state of electric vehicle market 2026" });
// Phase 2: Launch 5 parallel research subagents
await startAsyncSubagent({
task: `Research EV Market & Competition: search for "EV market share by manufacturer 2025 2026",
"electric vehicle sales global rankings", "Tesla BYD market share comparison".
Use webFetch on best results. Return findings with source URLs.`
});
await startAsyncSubagent({
task: `Research EV Battery Technology: search for "solid state battery progress 2026",
"EV battery cost per kwh trend", "lithium iron phosphate vs NMC comparison".
Use webFetch on best results. Return findings with source URLs.`
});
await startAsyncSubagent({
task: `Research EV Policy & Regulation: search for "EV tax credit policy 2026",
"emissions regulations electric vehicles", "EV tariffs trade policy".
Use webFetch on best results. Return findings with source URLs.`
});
await startAsyncSubagent({
task: `Research EV Charging Infrastructure: search for "EV charging network growth statistics",
"NACS vs CCS charging standard adoption", "fast charging stations by country".
Use webFetch on best results. Return findings with source URLs.`
});
await startAsyncSubagent({
task: `Research EV Consumer Economics: search for "EV total cost of ownership vs gas 2026",
"electric vehicle resale value trends", "EV adoption demographics income".
Use webFetch on best results. Return findings with source URLs.`
});
// Collect all results
const results = await waitForBackgroundTasks();
// Phase 3-5: Evaluate sources, cross-reference claims, synthesize into structured report
// Write comprehensive report with all findings and citations from all 5 subagents