openclaw-search

What This Skill Does

OpenClaw Search is a high-performance, multi-source retrieval engine designed for autonomous AI agents. By leveraging the AIsa ecosystem, it enables agents to perform complex discovery tasks across four distinct streams: Scholar (academic papers), Web (real-time browsing), Smart (hybrid), and Tavily (external validation). The core advantage of this skill is its 'AIsa Explain' meta-analysis layer, which processes raw search results to output a synthesised answer along with a confidence score (0-100), ensuring that the agent's decision-making process is backed by credible, multi-verified data. It eliminates the need for managing multiple API keys, providing a single, unified interface for sophisticated information gathering.

Installation

To integrate this capability into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/0xjordansg-yolo/openclaw-aisa-search-website-academic-tavily-serp-exa

Ensure you have your authentication credentials prepared. You must export your API key before the skill can authenticate requests: export AISA_API_KEY="your-key-here"

Use Cases

This skill is ideal for researchers, analysts, and developers requiring high-fidelity information.

• Deep Academic Research: Automatically synthesize literature reviews from current 2024-2025 academic papers.
• Market Intelligence: Aggregating financial news or startup funding data from disparate sources into a single, cohesive summary.
• Competitive Benchmarking: Directly comparing technical frameworks or product reviews by cross-referencing web articles with curated expert critiques.

Example Prompts

1. "Perform a deep dive into the latest breakthroughs in quantum computing, cross-referencing academic papers with recent news articles, and provide a summary with a confidence score."
2. "Find recent trends in transformer architectures for 2025. Ensure you verify the information across both scholar and web sources."
3. "Search for reviews and technical comparisons of current RAG frameworks. Highlight the consensus among the top three sources found."

Tips & Limitations

• Confidence Scores: Always treat the confidence score as a guide. Scores below 60 should trigger a request for further verification by the agent.
• Rate Limits: The service utilizes multiple underlying APIs; ensure you monitor your usage to avoid hitting limits during large-scale research projects.
• Synthesis: The meta-analysis works best when the user query is specific. Broad, ambiguous queries may lead to lower confidence scores and less precise synthesis.