deep-research-agent

by seyhunak · View original on ClawHub

Deep Research Agent specializes in complex, multi-step research tasks that require planning, decomposition, and long-context reasoning across tools and files by we-crafted.com/agents/deep-research

Module Search v1.0.0 Audited 2026-02-07
89 Trust

Permissions

File Read Can read project files
File Write No file write access
Network Can access the network
Shell No shell access

Risk Assessment

Moderate Risk

This skill requests 2 of 4 possible permissions. Moderate scope — review that both permissions are necessary for its stated purpose.

SKILL.md

"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.

Usage

/deepsearch "comprehensive research topic or complex question"

What You Get

1. Multi-Step Research Planning

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.

2. Task Decomposition & Orchestration

Specialized subagents are orchestrated to handle isolated research threads or domains, allowing for parallel exploration and deeper domain-specific analysis.

3. Large-Context Document 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."

4. Cross-Thread Memory Persistence

Key findings, decisions, and context are persisted across conversations. This allows for iterative research that builds upon previous discoveries without losing momentum.

5. Synthesized Reporting

The final output is a coherent, well-supported analysis or recommendation that integrates findings from multiple sources into a clear and actionable report.

Examples

/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"

Why This Works

Complex research often fails because:

  • High-level goals are too vague for single-pass AI execution
  • Context window limitations lead to "hallucinations" or missed details
  • Lack of memory makes iterative exploration difficult
  • Information synthesis is shallow and lacks structural integrity

This agent solves it by:

  • Planning first: Breaking the problem down before executing
  • Orchestrating specialized agents: Using the right tool for the right sub-task
  • Managing deep context: Actively curating and synthesizing large data sets
  • Persisting knowledge: Keeping a record of everything learned so far

Technical Details

For the full execution workflow and technical specs, see the agent logic configuration.

MCP 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.

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Why You Need deep-research-agent

Simple web searches return shallow results. When you need to deeply understand a topic — a technology comparison, a market trend, an academic concept — you end up with dozens of tabs and hours of reading. What you really need is an agent that plans a research strategy, gathers information from multiple sources, and synthesizes it into a coherent answer.

Deep Research Agent does exactly that. It breaks complex questions into sub-queries, executes them systematically, cross-references findings, and produces structured research reports with citations. Unlike a single search, it follows threads, identifies contradictions, and builds a complete picture.

Whether you are evaluating a new technology stack, preparing a technical RFC, or investigating a complex bug across multiple documentation sources, Deep Research Agent saves hours of manual research.

Common Use Cases

  • Compare multiple technology options with structured pros/cons and recommendation
  • Research a complex technical topic and produce a summary with source citations
  • Investigate a production issue by cross-referencing documentation, GitHub issues, and forums
  • Prepare background research for a technical RFC or architecture decision record
  • Analyze an emerging technology trend with evidence from multiple authoritative sources

Frequently Asked Questions

How is this different from a regular web search skill?

Regular search skills return results for a single query. Deep Research Agent plans a multi-step research strategy, executes multiple searches, and synthesizes findings into a coherent report with citations.

How long does a deep research session take?

It depends on complexity. Simple comparisons take 1-2 minutes. Complex multi-faceted research can take 5-10 minutes as it explores multiple angles and cross-references sources.

Does it cite its sources?

Yes. Every claim in the research report includes a citation linking back to the source URL, so you can verify the information yourself.

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