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Install in OpenClaw
/install xiaobai-deep-research
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- Flag outdated info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 internally coherent for automated research: it asks the agent to crawl public sources, summarize findings, and save reports. Before enabling it, consider: 1) how your agent runtime handles network access and rate limits (avoid unintended scraping or TOS violations); 2) where memory/research/ is stored and who can read those files (sensitive results could be persisted); 3) whether you want the agent to fetch paywalled or private content — that would require credentials which this skill currently does not request; and 4) verification practices: LLMs can hallucinate citations, so verify key claims and sources in generated reports. If any of these are concerns, review the agent's storage/network policies or limit the skill's autonomous invocation.
Capability Analysis
Type: OpenClaw Skill
Name: xiaobai-deep-research
Version: 1.0.0
The skill bundle contains only documentation and instructions (SKILL.md) for an AI agent to perform multi-source research and report generation. There is no executable code, and the instructions are consistent with the stated purpose of aggregating and synthesizing information from public sources like GitHub, ArXiv, and Reddit without any signs of malicious intent or data exfiltration.
Capability Assessment
Purpose & Capability
Name/description promise (aggregate, extract, synthesize research) matches the SKILL.md pipeline (multi-source search, extraction, synthesis, and report generation). No unrelated credentials, binaries, or installs are requested.
Instruction Scope
Instructions explicitly tell the agent to fetch content from public sources (web, GitHub, HN, ArXiv, Reddit, news), extract and synthesize it, and save reports to memory/research/. This is within the stated purpose. Minor note: the SKILL.md references writing files to a memory/research/ path even though no config paths are declared — this is common for agent memory but you may want to confirm how the agent's runtime implements and secures that storage.
Install Mechanism
Instruction-only skill with no install spec and no code files. Lowest install risk: nothing is written to disk by an installer or downloaded at install time.
Credentials
No environment variables, credentials, or config paths are requested. The actions described (web crawling and summarization) do not intrinsically require extra secrets. If you expect the skill to access paywalled sources or private repos, those would require credentials and are not currently declared.
Persistence & Privilege
always is false and the skill does not request to modify other skills or system settings. It instructs writing reports to agent memory/storage, which is normal for a research/reporting skill; confirm retention and sharing policies for that storage if you have privacy concerns.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install xiaobai-deep-research - After installation, invoke the skill by name or use
/xiaobai-deep-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: Multi-source research pipeline
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 82 downloads so far.
How do I install Deep Research Suite?
Run "/install xiaobai-deep-research" 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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