web-search

What This Skill Does

The web-search skill for OpenClaw provides a powerful interface for performing deep web research and granular content extraction. Powered by industry-leading engines like Tavily and Exa, this skill allows your AI agent to browse, search, and parse information from the live web. It excels at gathering context, verifying facts, and performing research-based tasks that require real-time data. Whether you need to synthesize the latest news on a specific technology, retrieve text from long-form research papers, or automate the aggregation of data across multiple URLs, this tool provides the foundational connectivity to the internet needed for complex reasoning workflows. By leveraging the inference.sh CLI, the skill abstracts the complexity of API management, letting you treat the web as a structured data source for your RAG pipelines.

Installation

You can integrate this skill into your environment by running the following command in your terminal:

clawhub install openclaw/skills/skills/okaris/web-search-2

Ensure that you have the infsh CLI installed and authenticated to interact with the underlying infrastructure. Once installed, the skill becomes immediately available for your agent to trigger.

Use Cases

• Automated Research Pipelines: Build workflows that perform periodic searches, extract findings, and pass them to an LLM for summarization or synthesis.
• Fact-Checking: Use the Exa Answer or Tavily Search Assistant features to verify claims or look up live data points during a conversation.
• Content Aggregation: Extract and clean text from technical documentation or articles to feed into a local knowledge base or vector store.
• Market Analysis: Monitor developments in specific sectors by querying the web for recent announcements and processing them through your AI agent.

Example Prompts

1. "Perform a research scan on the current state of large language model inference costs in 2024 and summarize the findings in a table."
2. "Search for the latest documentation on vector database benchmarks and extract the core performance metrics from the top 3 results."
3. "Go to the provided link and generate a concise 5-bullet point summary of the author's primary arguments regarding AI regulation."

Tips & Limitations

• Context Windows: When extracting large amounts of text from multiple URLs, be mindful of your LLM's context window limits; you may need to chain the extraction and summarization steps.
• Rate Limiting: Frequent and rapid-fire search requests may hit API rate limits imposed by the underlying providers. It is best practice to implement minor delays or batch your requests.
• Precision: While tools like Exa provide high-quality context, always instruct your agent to cross-reference data from multiple sources to ensure accuracy, especially for sensitive or highly technical subject matter.