Deep Research Suite

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name: deep-research-suite version: 1.0.0 description: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. emoji: 🔬 tags: [research, automation, productivity, analysis, ai-agent]

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Deep Research Suite 🔬

One command to aggregate, analyze, and synthesize research from multiple sources.

What It Does

Input: "Research AI agent memory management trends 2026"
Output:
1. Search 5+ sources
2. Extract key findings
3. Identify patterns
4. Generate structured report
5. Save to file for reference

Research Pipeline

Stage 1: Multi-Source Search

Sources to check:
Web search (general)
GitHub (code/examples)
Hacker News (discussions)
ArXiv (papers, if relevant)
Reddit (community opinions)
News sites (recent articles)

Stage 2: Content Extraction

For each source:
1. Fetch content
2. Extract main points
3. Identify key facts/statistics
4. Note source credibility
5. Tag by topic relevance

Stage 3: Synthesis

Combine findings:
Group by theme
Identify consensus views
Note contradictions
Highlight emerging trends
Flag outdated info

Stage 4: Report Generation

Output format:
Research Report: [Topic]
Date: YYYY-MM-DD
Sources: X sources analyzed
Executive Summary
[2-3 sentence overview]
Key Findings
Trend 1: [Name]
Source: X, Y, Z
Evidence: ...
Implications: ...
Trend 2: [Name]
...
Contradictions / Debates
View A says: ... (Source: X)
View B says: ... (Source: Y)
Assessment: ...
Actionable Insights
1. ...
2. ...
Sources
Source 1 - relevance: high
Source 2 - relevance: medium

Usage Examples

Quick Research

User: "What's new in LLM context window management?"
Agent:
[Searching 5 sources...]
[Extracting key findings...]
[Synthesizing...]
LLM Context Window Management - 2026 Update
Key Trends
1. Dynamic Context Sizing - Models now auto-adjust context based on task complexity
2. Semantic Chunking - Replace fixed-size chunks with meaning-based segments
3. Context Compression - 10x compression with minimal info loss
[Full report saved to memory/research/llm-context-2026-04-21.md]

Deep Dive

User: "Deep research on AI agent subagent architecture patterns"
Agent:
[This will take 5-10 minutes for comprehensive analysis]
Stage 1/4: Multi-source search...
GitHub: 15 repos found
Papers: 3 relevant
Discussions: 12 threads
Stage 2/4: Content extraction...
Analyzing top 10 GitHub repos...
Extracting paper findings...
Summarizing discussions...
Stage 3/4: Synthesis...
Identified 4 architecture patterns
Found 2 debates
3 emerging trends
Stage 4/4: Report generation...
[Report saved to memory/research/subagent-architecture-2026-04-21.md]

Competitive Analysis

User: "Compare popular AI agent memory solutions"
Agent:
[Generating comparison matrix...]
| Solution | Approach | Pros | Cons | Stars |
|----------|----------|------|------|-------|
| Mem0 | Persistent memory | Easy integration | Limited context | 25k |
| Letta | Stateful agents | Full state | Complex setup | 15k |
| LangGraph | Graph memory | Flexible | Learning curve | 100k |
[Full comparison saved to memory/research/memory-solutions-comparison.md]

Output Files

All research saved to memory/research/:

memory/research/
├── llm-context-2026-04-21.md
├── subagent-architecture-2026-04-21.md
└── memory-solutions-comparison.md

Integration with Other Skills

Workflow Checkpoint - Research is a multi-step workflow

Memory Guard - Save key findings to long-term memory

Content Creator - Generate polished reports

Anti-Patterns

❌ Don't rely on single source ❌ Don't skip source credibility check ❌ Don't present outdated info as current ❌ Don't fabricate sources or statistics

License

MIT