- 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]
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 referenceResearch 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 relevanceStage 3: Synthesis
Combine findings:
- Group by theme
- Identify consensus views
- Note contradictions
- Highlight emerging trends
- Flag outdated infoStage 4: Report Generation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](url) - relevance: high
- [Source 2](url) - relevance: mediumUsage 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.mdIntegration 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