deep-research

What This Skill Does

The Deep Research Agent is an advanced analytical engine designed to tackle complex, multi-layered inquiries that standard search tools cannot handle. By utilizing structured decomposition, the agent breaks high-level, vague objectives into granular, executable research threads. It orchestrates specialized sub-agents to perform parallel information gathering, ensuring that domain-specific nuances are captured effectively. Once the data is retrieved, the agent leverages advanced long-context reasoning to synthesize vast amounts of documentation into a coherent, actionable report. This agent is built to eliminate the 'shallow research' trap, providing a robust, data-driven synthesis rather than just a collection of links. It acts as an autonomous analyst that maintains memory across threads, allowing users to conduct iterative investigations that evolve over time.

Installation

To integrate the Deep Research Agent into your OpenClaw environment, ensure you have the necessary permissions enabled for external data access. Execute the following command in your terminal:

clawhub install openclaw/skills/skills/asterisk622/xiaoding-deep-research

Use Cases

• Strategic Market Analysis: Deconstruct industry shifts by analyzing global supply chains, regulatory changes, and competitive landscapes simultaneously.
• Technical Due Diligence: Perform deep-dives into software architecture, such as analyzing the security implications of Kubernetes infrastructure or emerging blockchain protocols.
• Literature Review Synthesis: Compile insights from extensive technical reports or documentation sets that exceed standard prompt limits, distilling them into high-level summaries.
• Long-term Project Planning: Map out complex research trajectories by breaking down large goals into weekly milestones and executable sub-tasks.

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

• Be Specific: While the agent handles complexity well, providing a clear scope (e.g., specific timeframes or regions) improves synthesis quality.
• Iterative Feedback: Treat the output as a draft; if the agent misses a specific nuance, provide feedback to redirect the sub-agents for more targeted analysis.
• Data Privacy: This tool accesses external APIs and file systems. Ensure you are not processing highly sensitive or non-public credentials through the search interface.
• Context Limit: While the agent handles 'long-context,' extremely broad topics may still require manual steering to prevent hallucination in the synthesis phase.