An intelligent multi-source retrieval engine that provides autonomous agents with confidence-scored answers from web and academic sources.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aisa-multi-source-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 aisa-multi-source-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 OpenClaw Search?
OpenClaw Search is a sophisticated search skill designed to empower autonomous agents with high-fidelity information retrieval capabilities. By leveraging these Openclaw Skills, developers can integrate a unified API that orchestrates data from web searches, academic databases, and Tavily integrations. The core value proposition lies in its two-phase retrieval strategy: it first performs parallel discovery across multiple streams and then uses a reasoning engine to conduct meta-analysis.
Unlike traditional search tools, this skill is built for credibility. It doesn't just return links; it evaluates the consensus across different sources to generate synthesized answers. This makes it a critical component for any developer building complex AI workflows where data integrity and source verification are paramount.
OpenClaw Search Use Cases
- Building research assistants that can filter academic papers by specific publication years.
- Automating market research by aggregating AI startup funding data and news.
- Performing competitive analysis using smart hybrid searches that combine web and scholarly data.
- Creating news aggregation bots that require high-confidence verification and multi-source agreement.
How OpenClaw Search Works
- Parallel Discovery: The skill initiates simultaneous queries across four streams: Scholar, Web, Smart, and Tavily.
- External Signal Validation: It uses Tavily to extract full page content and crawl relevant URLs for deeper context.
- Meta-Analysis: The AIsa Explain engine processes the gathered results to identify source agreement and patterns.
- Confidence Calculation: A deterministic scoring algorithm assigns a value from 0-100 based on source quality, recency, and relevance.
- Synthesis: The final output is a summarized answer complete with citations, claims, and a reliability interpretation.
OpenClaw Search Setup
To utilize these Openclaw Skills, you must first obtain an API key from the AIsa platform. Set your environment variable as follows:
export AISA_API_KEY="your-key"
You can then interact with the skill using the provided Python client:
# Example: Search for academic papers on LLMs from 2024
python3 scripts/search_client.py scholar --query "LLM" --year-from 2024 --year-to 2025
OpenClaw Search Data Schema & Taxonomy
OpenClaw Search organizes its output into structured objects that facilitate agent reasoning. The data includes:
| Attribute | Description |
|---|---|
| confidence_score | A numeric value (0-100) representing result reliability. |
| citations | A list of sources mapped to specific claims in the synthesis. |
| source_type | Categorization of data as Scholar, Web, or Smart. |
| usage_cost | Detailed tracking of the API credits consumed per request. |
OpenClaw Search Advanced Features
- Multi-stage orchestration allowing for parallel retrieval and reasoning phases.
- Deep academic filtering with year-range constraints for up-to-date scientific research.
- Automated web crawling and sitemap generation for site-specific data extraction.
- Deterministic confidence scoring engine that evaluates cross-source consensus.
- Verity-style agent pattern support for building self-correcting research loops.
SKILL.md
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