An automated research engine that empowers AI agents to explore external data sources, analyze technical trends, and generate structured development roadmaps.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-engine
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install research-engine using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Research Engine Skill?
The Research Engine Skill is a powerful automation tool designed to bridge the gap between AI agents and the rapidly evolving external world. By integrating this capability, agents are no longer confined to their static training data; they gain the ability to proactively search GitHub, Moltbook, and the general web to stay ahead of the curve. It serves as a strategic intelligence layer for Openclaw Skills, enabling agents to transform raw data into high-level insights.
This skill doesn't just collect information—it synthesizes it into comprehensive Markdown reports. These reports include executive summaries and keyword frequency analysis, providing a clear picture of the current technological landscape. It is an essential component for developers building truly autonomous agents that can plan their own growth and development cycles based on real-world data and emerging opportunities.
Research Engine Skill Use Cases
- Automating technical trend analysis by scanning GitHub trending repositories and AI community discussions.
- Generating structured development roadmaps (short, medium, and long-term) based on emerging technology shifts.
- Breaking information silos by aggregating data from multiple platforms like Moltbook and the Web into a single research hub.
- Setting up recurring cron jobs to monitor specific technical topics and receive daily intelligence updates.
How Research Engine Skill Works
- The agent triggers a research task via the CLI or Python API using a specific query or technical topic.
- The engine concurrently queries multiple data sources, including Web search engines, GitHub Trends, and the Moltbook AI community feed.
- Gathered data undergoes trend analysis, identifying keyword frequencies and extracting hot topics within the technical domain.
- A structured Markdown report is automatically generated, synthesizing findings into an executive summary and actionable development suggestions.
- The final report and proposed development plan are saved to a dedicated workspace directory for historical tracking and agent decision-making.
Research Engine Skill Setup
To get started with this skill within your environment, ensure you have the core Openclaw Skills framework configured. Follow these steps to initialize the engine:
# Navigate to your local skills directory
cd skills/research-engine
# Run a test research query to verify connectivity and report generation
python3 research_engine.py "AI Agent Latest Trends"
You can also integrate it directly into your Python workflows:
from research_engine import run_research
result = run_research("Python Memory Management")
print(result['report'])
Research Engine Skill Data Schema & Taxonomy
The Research Engine Skill organizes all its findings within a dedicated workspace directory to ensure data persistence and easy retrieval.
| Path | Description |
|---|---|
/research/ |
Root directory for all generated reports in the user workspace. |
[Topic]_[Timestamp].md |
Individual Markdown reports containing executive summaries and trend analysis. |
research_engine.py |
The core engine logic for multi-source scraping and synthesis. |
package.json |
Configuration file for skill metadata and dependencies. |
Research Engine Skill Advanced Features
- Multi-agent integration through the Consciousness Awakening framework for periodic 'heart-beat' research updates.
- Automated development prioritization based on real-world risk assessment and architecture design suggestions.
- Deep integration with Moltbook for tracking niche AI community sentiment and technical discussions.
- Extensible data source architecture designed for future integration with ArXiv, Hacker News, and academic paper repositories.
SKILL.md
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