deep-research

What This Skill Does

The Deep Research Agent is a powerful cognitive tool designed to transform high-level, complex inquiries into actionable, evidence-based reports. Unlike standard search tools that perform a single pass, this agent utilizes a recursive 'plan-then-execute' architecture. It decomposes broad, ambiguous objectives into granular research sub-tasks. By orchestrating specialized subagents, it manages parallel investigation threads, ensuring that every facet of a question is explored thoroughly. It excels at long-context reasoning, enabling it to ingest massive datasets, documentation, and search results to synthesize information without losing critical details. With persistent cross-thread memory, it maintains the context of your investigation, allowing for iterative, deep-dive research sessions that evolve over time.

Installation

To integrate the Deep Research Agent into your OpenClaw environment, ensure your MCP configuration is set to include the core skill package. Run the following command in your terminal:

clawhub install openclaw/skills/skills/seyhunak/deep-research

Ensure your permissions allow the agent to manage temporary files and network requests, as the agent requires these to perform web-based lookups and cross-reference documentation.

Use Cases

This skill is built for professionals and researchers who need more than just a quick summary. It is ideal for:

• Conducting technical feasibility studies on emerging technologies.
• Performing competitive market analysis by aggregating data across multiple public sources.
• Navigating complex architectural debates in software engineering, such as comparing cloud infrastructure providers or security frameworks.
• Summarizing long-form technical reports or white papers into structured, executive-level briefs.
• Mapping supply chain dynamics or global economic trends over decade-long horizons.

Example Prompts

1. /deepsearch "Analyze the security implications and performance trade-offs of using eBPF for observability in a multi-tenant Kubernetes cluster."
2. /deepsearch "Research the projected shift in global rare earth metal dependency for the next 10 years given current trends in solid-state battery development."
3. /deepsearch "Synthesize a comprehensive report on the current state-of-the-art for autonomous AI agents, focusing on their adoption in enterprise workflows and potential integration challenges."

Tips & Limitations

• Planning Phase: The agent consumes time to plan; do not interrupt the initial decomposition stage as this setup is critical for high-quality output.
• Clarity Matters: While the agent is smart, providing a clear boundary for the research (e.g., 'focus on the last 5 years' or 'exclude academic papers') improves result precision.
• Scope: Avoid using this for simple fact-finding tasks; standard search agents are faster for questions like 'What is the current price of Bitcoin?'.
• Limitations: Large-context reasoning is powerful, but extremely proprietary or gated documentation may require manual file ingestion if the agent cannot access it via standard network protocols.