openclaw-serper

What This Skill Does

The openclaw-serper skill is a powerful web-retrieval engine for the OpenClaw agent, bridging the gap between a standard search engine and a deep-content reader. Unlike basic search tools that return sparse snippets or links, this skill uses the Serper API to perform high-quality Google searches and automatically pipes the results through the trafilatura library. This ensures that every result provided to the agent includes the clean, full-text content of the webpage, stripping away navigation menus, ads, and boilerplate code.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/nesdeq/openclaw-serper

Use Cases

This skill is designed for scenarios requiring deep insight rather than just URLs. It is ideal for:

• Technical Research: Finding documentation, API references, or debugging patterns.
• Product Analysis: Comparing features between different software or hardware solutions.
• Market Intelligence: Tracking breaking news, industry announcements, or current events using the 'current' mode.
• Evergreen Knowledge: Explaining complex concepts using the 'default' mode to aggregate information from authoritative sources.

Example Prompts

1. "What are the main performance differences between React Server Components and traditional client-side rendering? Find technical documentation and summarize the architectural changes."
2. "What are the latest updates regarding the OpenSource AI policy announced by the EU this week?"
3. "Compare the pricing and cloud storage features of Google Drive, Dropbox, and iCloud for a small business user."

Tips & Limitations

• Quality Over Quantity: The tool is designed to work with one well-crafted query. Running multiple redundant searches is unnecessary because the tool already retrieves full page content for every result found.
• Mode Selection: Always differentiate between default for evergreen knowledge and current for time-sensitive, news-based queries.
• Locale Awareness: If a user requests information in a non-English language or specifies a geographic location, you must explicitly set the --gl and --hl flags to ensure regional accuracy. Failure to set these in non-English contexts may result in inaccurate or poorly localized search results.
• Efficiency: The system limits the use of this skill to two calls per interaction to ensure optimal agent performance and token usage. Read the provided text thoroughly before deciding if a second search is actually required.