Conduct deep research with multi-source analysis, generating professional reports. Use when user needs comprehensive research, market analysis, competitive analysis, technical investigation, or detailed reports. Supports web search, academic sources, fact verification, and PDF/Markdown output. Works like ChatGPT Deep Research or Claude Research mode.
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name
research-orchestrator
description
Conduct deep research with multi-source analysis, generating professional reports. Use when user needs comprehensive research, market analysis, competitive analysis, technical investigation, or detailed reports. Supports web search, academic sources, fact verification, and PDF/Markdown output. Works like ChatGPT Deep Research or Claude Research mode.
CRITICAL: Use sessions_spawn to create parallel research agents.
For each research angle, spawn a sub-agent:
sessions_spawn with:
- session: "research-$TASK_ID-{angle_id}"
- mode: "run"
- task: Research instructions for this angle
- streamTo: "parent" (for progress visibility)
Research Agent Instructions Template:
You are a research agent. Your task: {angle_name}
Search Queries:
{queries}
Instructions:
1. Use web-search skill to search each query
2. For each result, extract key facts
3. Note source URL and assess credibility
4. Save findings to: $TASK_DIR/research/{angle_id}.md
Format findings as:
## {angle_name}
### Key Findings
- Finding 1 [Source: url] (Confidence: High)
- Finding 2 [Source: url] (Confidence: Medium)
### Data Points
| Metric | Value | Source | Confidence |
|--------|-------|--------|------------|
### Sources
1. [Source Name](url) - Credibility: High
Step 4: Iterative Search (Gap Analysis)
After initial research completes, analyze gaps:
# Analyze what's missing
gaps = identify_gaps(task_dir)
# For each gap, spawn another searchfor gap in gaps:
sessions_spawn with additional queries
Step 5: Fact Verification
Cross-verify key claims:
For each major finding:
1. Check if multiple sources agree
2. Assess source credibility
3. Flag conflicting information
4. Assign confidence score
Step 6: Deep Analysis
Synthesize research into insights:
Analysis should include:
1. Key Insights - Most important findings
2. Trend Analysis - Where things are heading
3. Risk Assessment - What could go wrong
4. Opportunities - Where to focus
Step 7: Generate Report
Create professional report with:
# Generate Markdown report
python3 $TASK_DIR/../scripts/report_generator.py generate $TASK_DIR {lang}
# Convert to PDF
python3 $TASK_DIR/../scripts/pdf_converter.py convert $TASK_DIR/output/report.md
Real Execution Example
User: "帮我深度研究一下2026年AI芯片市场"
Agent should:
1. Create task structure
mkdir -p tasks/20260323_100000/{research,analysis,output}
2. Decompose into angles
- market_size, competitors, technology, investment
3. Launch 4 parallel sub-agents using sessions_spawn
Agent 1 (market_size):
"Search: 'AI chip market size 2026', '半导体市场规模预测'
Find market size data, growth rates, forecasts
Save to tasks/.../research/market_size.md"
Agent 2 (competitors):
"Search: 'NVIDIA AMD Intel AI chips market share'
Analyze key players, their strategies
Save to tasks/.../research/competitors.md"
Agent 3 (technology):
"Search: 'AI chip architecture trends 2026', 'GPU NPU发展'
Identify technology trends
Save to tasks/.../research/technology.md"
Agent 4 (investment):
"Search: 'AI chip investment 2026', 'AI芯片投融资'
Track investment and M&A activity
Save to tasks/.../research/investment.md"
4. Wait for agents to complete (stream progress)
5. Verify facts across sources
6. Generate analysis report
7. Create final report (MD + PDF)
8. Output:
📄 report.md
📄 report.pdf
