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Deep Research Suite

Name: Deep Research Suite
Author: aptratcn

作者 Erwin · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ✓ 安全检测通过

总下载

当前安装

版本数

在 OpenClaw 中安装

/install xiaobai-deep-research

功能描述

Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report.

使用说明 (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- Flag outdated info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

安全使用建议

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.

功能分析

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.

能力评估

✓ 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.

ℹ 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.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install xiaobai-deep-research
安装完成后，直接呼叫该 Skill 的名称或使用 /xiaobai-deep-research 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

Initial release: Multi-source research pipeline

元数据

Slug xiaobai-deep-research

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题