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/install deep-research-suite
Description
Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report.
README (SKILL.md)
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- Flag outdated info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](url) - relevance: high
- [Source 2](url) - 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
Usage Guidance
This skill is coherent with its stated purpose, but before installing check: (1) whether your agent runtime allows web access and has permission to write to the memory/research/ location (the SKILL.md assumes that), (2) whether you want the agent to autonomously run repeated web searches (you can disable autonomous invocation if concerned), and (3) that saving aggregated content may store copyrighted or sensitive material—review reports and sources for confidentiality and accuracy. If you need access to paywalled/private sources, expect additional credential requests at that time.
Capability Analysis
Type: OpenClaw Skill
Name: deep-research-suite
Version: 1.0.0
The 'deep-research-suite' bundle contains only metadata and documentation (SKILL.md) describing a multi-stage research workflow. It lacks executable code and its instructions focus entirely on legitimate research tasks such as searching public sources (GitHub, ArXiv, etc.) and generating reports, with no evidence of malicious intent or prompt injection.
Capability Assessment
Purpose & Capability
The name/description match the SKILL.md: it instructs web/GitHub/HN/ArXiv/Reddit/news searches, extraction, synthesis, and saving reports. No unrelated binaries, credentials, or config paths are requested.
Instruction Scope
Instructions are limited to searching public sources, extracting and synthesizing content, and saving reports to memory/research/. The SKILL.md references writing files to memory/research/ but the skill declares no config paths — this assumes the agent runtime grants write access to its memory/filesystem. The instructions do not ask to read unrelated local files or secrets and explicitly warn against fabricating sources.
Install Mechanism
No install spec or code files (instruction-only). This is low-risk: nothing is downloaded or written to disk by an installer.
Credentials
The skill requires no environment variables, credentials, or external config. That is proportionate for a research-aggregation instruction set. (If the agent were to access paywalled or private sources, additional credentials would be needed, but none are requested here.)
Persistence & Privilege
always is false and no special privileges are requested. The skill can be invoked autonomously by the agent (platform default) but it does not request elevated or persistent system-wide privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install deep-research-suite - After installation, invoke the skill by name or use
/deep-research-suite - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: One command to aggregate, analyze, and synthesize research from multiple sources
Metadata
Frequently Asked Questions
What is Deep Research Suite?
Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. It is an AI Agent Skill for Claude Code / OpenClaw, with 105 downloads so far.
How do I install Deep Research Suite?
Run "/install deep-research-suite" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Deep Research Suite free?
Yes, Deep Research Suite is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Deep Research Suite support?
Deep Research Suite is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Deep Research Suite?
It is built and maintained by Erwin (@aptratcn); the current version is v1.0.0.
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