openclaw-search

What This Skill Does

OpenClaw Search is a high-performance, multi-source retrieval engine designed specifically for autonomous AI agents. By integrating web, academic, and specialized search streams (including Tavily), it provides a unified interface for gathering information with a high degree of confidence. Unlike standard search tools that provide raw links, OpenClaw Search utilizes the 'AIsa Explain' meta-analysis layer to synthesize data and provide a confidence score (0-100), ensuring that agents can distinguish between reliable facts and low-trust information.

Installation

To add this skill to your OpenClaw environment, ensure your terminal is authenticated and run the following installation command:

clawhub install openclaw/skills/skills/bowen-dotcom/aisa-search-skill

Once installed, ensure you have your API key set in your environment variables to authorize requests:

export AISA_API_KEY="your-key"

Use Cases

• Academic Research: Quickly synthesize findings from global research databases for literature reviews or scientific exploration.
• Market Intelligence: Monitor startup funding, industry shifts, and competitive moves by aggregating news and financial data.
• Technical Synthesis: Use the 'Smart' mode to perform RAG-style deep dives into complex documentation or frameworks.
• News Verification: Cross-reference information across multiple search modalities to filter out misinformation and provide verified summaries.

Example Prompts

1. "Perform a deep dive into the current state of RAG frameworks in 2025; compare three top options based on documentation and community sentiment."
2. "Search for the latest peer-reviewed papers on transformer architectures, specifically looking for efficiency optimizations in long-context windows."
3. "Synthesize recent news regarding quantum computing breakthroughs from the last month, and score the reliability of these reports."

Tips & Limitations

• Confidence Scores: Pay close attention to the confidence score provided; anything below 70 should be treated as speculative.
• Rate Limiting: Because this skill queries multiple endpoints, consider the cumulative cost if your agent is performing high-frequency, automated research loops.
• Scope: While powerful, the skill relies on the availability of its source providers. If the Tavily or Scholar endpoints are unreachable, the meta-analysis may be based on reduced inputs.