deep-research

What This Skill Does

The Deep Research Agent is an advanced analytical engine designed to solve the problem of superficial AI responses. Instead of providing a single-pass answer, this skill decomposes complex, multi-layered queries into a structured research roadmap. It orchestrates specialized sub-tasks to gather, synthesize, and analyze data from across your files, web search results, and provided context. By maintaining a persistent memory across threads, the agent allows you to iterate on research findings, ensuring that no insight is lost during the investigative process. It effectively manages large-context windows, allowing it to act as a sophisticated research assistant for projects requiring heavy documentation review or multi-domain investigation.

Installation

To integrate the Deep Research Agent into your OpenClaw workflow, ensure you have the necessary permissions configured. You can install this skill directly via the CLI using the following command: clawhub install openclaw/skills/skills/bobdevibecoder/bobagent-deep-research. This agent is sourced from the openclaw/skills repository, maintained by bobdevibecoder. Once installed, you will immediately have access to the /deepsearch command for your agent interactions.

Use Cases

This skill is ideal for professionals and developers who deal with high-complexity inquiries. Use it to perform exhaustive market research, such as analyzing competitive landscapes or technological trends. It is equally effective for technical deep-dives, such as evaluating the security implications of architectural shifts in Kubernetes or cloud infrastructure. Furthermore, it excels in academic or long-form documentation synthesis, where the agent can ingest multiple PDFs or internal reports to build a cohesive narrative based on the provided material.

Example Prompts

1. /deepsearch "Perform an audit of the current regulatory landscape for AI-driven financial services in the European Union and compare it with North American frameworks."
2. /deepsearch "Research the long-term impact of decentralized identity protocols on enterprise SSO adoption; provide a summary of potential risks and integration strategies."
3. /deepsearch "Analyze these three project specification documents and generate a list of potential security bottlenecks for our upcoming migration to a serverless architecture."

Tips & Limitations

To get the best results, start with a high-level objective, as the agent is optimized for decomposition. While the agent manages long-context reasoning well, ensure your input files are categorized or clearly labeled to assist the agent in its retrieval process. Note that because this agent performs autonomous research, it requires external network access to crawl the web; monitor your API usage if you are on a restricted plan. For highly sensitive tasks, review the synthesis output for alignment with internal compliance policies, as the agent may occasionally synthesize information from a broad range of sources.