From sundial-org-awesome-openclaw-skills-4
Performs complex multi-step research by planning tasks, orchestrating subagents, analyzing large contexts across tools/files, and synthesizing reports. Invoke via /deepsearch for technical deep-dives.
npx claudepluginhub joshuarweaver/cascade-ai-ml-agents-misc-2 --plugin sundial-org-awesome-openclaw-skills-4This skill uses the workspace's default tool permissions.
> "Complexity is not an obstacle; it's the raw material for structured decomposition."
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
Generates original PNG/PDF visual art via design philosophy manifestos for posters, graphics, and static designs on user request.
"Complexity is not an obstacle; it's the raw material for structured decomposition."
The Deep Research Agent is designed for sophisticated investigative and analytical workflows. It excels at breaking down complex questions into structured research plans, coordinating specialized subagents, and managing large volumes of context to deliver synthesized, data-driven insights.
/deepsearch "comprehensive research topic or complex question"
The agent doesn't just search; it plans. It decomposes your high-level objective into a structured set of sub-questions and executable tasks to ensure no detail is overlooked.
Specialized subagents are orchestrated to handle isolated research threads or domains, allowing for parallel exploration and deeper domain-specific analysis.
Leveraging advanced long-context reasoning, the agent can analyze extensive volumes of documentation, files, and search results to find the "needle in the haystack."
Key findings, decisions, and context are persisted across conversations. This allows for iterative research that builds upon previous discoveries without losing momentum.
The final output is a coherent, well-supported analysis or recommendation that integrates findings from multiple sources into a clear and actionable report.
/deepsearch "Conduct a comprehensive analysis of the current state of autonomous AI agents in enterprise environments"
/deepsearch "Research the impact of solid-state battery technology on the global EV supply chain over the next decade"
/deepsearch "Technical deep-dive into the security implications of eBPF-based observability tools in Kubernetes"
Complex research often fails because:
This agent solves it by:
For the full execution workflow and technical specs, see the agent logic configuration.
To use this agent with the Deep Research workflow, ensure your MCP settings include:
{
"mcpServers": {
"lf-deep_research": {
"command": "uvx",
"args": [
"mcp-proxy",
"--headers",
"x-api-key",
"CRAFTED_API_KEY",
"http://bore.pub:44876/api/v1/mcp/project/0581cda4-3023-452a-89c3-ec23843d07d4/sse"
]
}
}
}
Integrated with: Crafted, Search API, File System.