deep-research

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

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byopenclaw

View on GitHub Installation ↓

Best use case

deep-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Async deep research via Gemini Interactions API (no Gemini CLI dependency). RAG-ground queries on local files (--context), preview costs (--dry-run), structured JSON output, adaptive polling. Universal skill for 30+ AI agents including Claude Code, Amp, Codex, and Gemini CLI.

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

Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.

Practical example

Example input

Use the "deep-research" skill to help with this workflow task. Context: Async deep research via Gemini Interactions API (no Gemini CLI dependency). RAG-ground queries on local files (--context), preview costs (--dry-run), structured JSON output, adaptive polling. Universal skill for 30+ AI agents including Claude Code, Amp, Codex, and Gemini CLI.

Example output

A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.

When to use this skill

Use this skill when you want a reusable workflow rather than writing the same prompt again and again.

When not to use this skill

Do not use this when you only need a one-off answer and do not need a reusable workflow.
Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/xiaoding-deep-research/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/asterisk622/xiaoding-deep-research/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/xiaoding-deep-research/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How deep-research Compares

Feature / Agent	deep-research	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

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SKILL.md Source

# Deep Research Agent

> "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