A high-performance web search and content extraction tool for AI agents utilizing the Querit.ai API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install querit-search
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 querit-search 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 Querit Search?
Querit Search is a specialized integration designed to give AI agents the ability to perform web searches and extract readable markdown content without needing a full browser environment. By leveraging the Querit.ai API, this skill provides structured data including titles, URLs, snippets, and page timestamps, making it an essential component for agents that need to stay updated with real-time facts or documentation.
Built as part of the ecosystem of Openclaw Skills, this tool focuses on delivering clean, parseable information. It allows for granular control over search parameters, including language localization, country-specific results, and domain-based filtering, ensuring that the AI agent receives the most relevant information for its task.
Querit Search Use Cases
- Finding the latest software documentation, API references, or coding tutorials.
- Retrieving current events, news updates, or fact-checking information.
- Targeted research across specific domains using inclusion and exclusion filters.
- Extracting the main readable text from any URL directly into markdown format for LLM processing.
- Automated data gathering for research workflows that require high-speed search without interactive browsing.
How Querit Search Works
- The AI agent or user initiates a query through the command-line interface with optional flags for filtering.
- The skill processes the request and communicates with the Querit.ai API using a secure API key.
- Search results are retrieved, featuring titles, snippets, and metadata like page age.
- If content extraction is requested, the skill fetches the target URL and parses the main body into markdown.
- The final output is returned in a structured text format or raw JSON, depending on the agent's requirements.
Querit Search Setup
To integrate this search capability into your project, ensure you have an API key from Querit.ai.
- Get a free API key at https://querit.ai.
- Set the
QUERIT_API_KEYenvironment variable in your system. - Install the required Node.js dependencies:
npm install
- Verify the installation by running a simple query:
node search.js "Openclaw Skills" --json
Querit Search Data Schema & Taxonomy
The skill organizes results into structured formats compatible with modern data pipelines. When using the JSON output mode, the following schema is followed:
| Property | Description |
|---|---|
url |
The direct link to the indexed content. |
title |
The page title provided by the search engine. |
snippet |
A brief textual preview of the page content. |
page_age |
A human-readable string indicating how recently the page was updated. |
page_time |
A machine-readable timestamp for precise data sorting. |
When content extraction is enabled, the output includes a markdown block containing the extracted headings and body text of the webpage.
Querit Search Advanced Features
- Temporal filtering using the
--dateflag to find content from the last day, week, month, or year. - Domain-specific search allowing up to 20 domains to be included or excluded from results.
- Multi-language and multi-country support for localized information gathering.
- Direct markdown extraction utility via content.js for deep reading of web articles.
- High-volume search capability supporting up to 100 results per single query, ideal for large-scale Openclaw Skills automation tasks.
SKILL.md
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