A strategic research framework for AI agents to conduct iterative, multi-source web investigations and generate structured technical reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install call-web-search-agent-strategy
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 call-web-search-agent-strategy 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 Call Web Search Agent Strategy?
The Call Web Search Agent Strategy is a sophisticated skill designed for high-fidelity information acquisition within the Openclaw Skills ecosystem. It operates on the principle of Absolute Tool Priority, ensuring that real-time data from external sources always supersedes outdated internal knowledge. By implementing a structured two-phase workflow, this skill enables agents to move from broad preliminary searches to deep, focused iterations without losing track of source authority.
This skill is particularly valuable for developers and researchers who require up-to-the-minute technical data, market intelligence, or software versioning details. It enforces strict organizational standards, including mandatory inline citations and automated bibliography generation, ensuring that every research log produced is both verifiable and professional.
Call Web Search Agent Strategy Use Cases
- Verifying technical specifications for newly released software or hardware that exceeds an agent's training data cut-off.
- Conducting competitive market analysis using real-time financial indicators and social media trends.
- Performing deep-dive academic or technical literature reviews via automated URL scraping and synthesis.
- Building comprehensive, citation-heavy research logs for complex multi-step technical queries.
How Call Web Search Agent Strategy Works
- Setup and Preliminary Plan: The agent formulates 3-5 core search angles and initializes a dedicated Research Log using wiki tools.
- Breadth Search: Parallel execution of search tools across diverse platforms, prioritizing specialized finance and social media tools when necessary.
- Deep Reading: The agent selects the most authoritative URLs (Official > arXiv > Tech Media) for full-text scraping and extracts critical numbers and parameters.
- Iterative Focus: A continuous loop identifies the most important next question based on current gaps, searching and recording data until the objective is met.
- Synthesis and Submission: All unique URLs are compiled into a global bibliography, and the final research log is submitted through a formal results tool.
Call Web Search Agent Strategy Setup
To integrate this capability into your Openclaw Skills environment, ensure your agent has access to the wiki and search toolsets. Install the strategy using the following command:
# Add the search strategy to your agent configuration
openclaw-agent install call-web-search-agent-strategy
Configure your environment variables to allow url_scraping and execute_search_tool permissions for the agent.
Call Web Search Agent Strategy Data Schema & Taxonomy
The skill manages data through a structured wiki-based approach to ensure transparency and traceability within Openclaw Skills.
| Data Component | Format | Description |
|---|---|---|
| Research Log | Markdown (.md) | A living document created at wiki/ containing all iterative findings. |
| Inline Citations | [[Source](URL)] |
Mandatory attribution format required for every independent fact or data point. |
| Global Bibliography | Numbered List | A deduplicated list of all unique URLs accessed during the research lifecycle. |
| Submission Metadata | JSON | Contains the file path and a summary message in the target output language. |
Call Web Search Agent Strategy Advanced Features
- Deadlock Handling: Automatically terminates invalid, high-cost cyclical behaviors if a sub-goal fails after two consecutive attempts.
- Dynamic Cost Control: Implements a hard limit of 5000 words for research logs to optimize token usage and processing time.
- Domain-Specific Prioritization: Intelligently switches between general search and specialized financial or social media discovery tools based on query context.
- Source Hierarchy Evaluation: Automatically ranks information reliability by source type, ensuring official documentation and whitepapers take precedence over blogs or forums.
SKILL.md
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