# Deep Dive

Claude-native deep research using DAG-based query planning, parallel subagent...

- Repository: https://github.com/rohitg00/awesome-claude-code-toolkit (2800 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-05-12
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add rohitg00/awesome-claude-code-toolkit --skill deep-dive -g -a claude-code -y`
Codex: `npx skills add rohitg00/awesome-claude-code-toolkit --skill deep-dive -g -a codex -y`
Cursor: `npx skills add rohitg00/awesome-claude-code-toolkit --skill deep-dive -g -a cursor -y`
Gemini CLI: `npx skills add rohitg00/awesome-claude-code-toolkit --skill deep-dive -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add rohitg00/awesome-claude-code-toolkit`

## Description

Claude-native deep research using DAG-based query planning, parallel subagent execution, and gap-driven iteration. No external API needed.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Deep Dive

Autonomous deep research using the same DAG-based planning pattern as Google's Deep Research, but running entirely on Claude Code with no external dependencies.

## How it works

1. **Plan**, decompose the question into a DAG of sub-questions with dependencies
2. **Fan out**, run independent sub-questions in parallel via Agent subagents
3. **Gap analysis**, each subagent returns findings + identified gaps
4. **Iterate**, gaps become new sub-questions, fed back into the DAG
5. **Synthesize**, once all nodes complete, produce a final report

## Steps

### 1. Decompose into a DAG

Given the research question, generate a DAG of sub-questions. Each node has:
- **id**: short identifier (e.g., `q1`, `q2a`)
- **question**: the specific sub-question to research
- **depends_on**: list of node IDs whose answers are needed first (empty = no dependencies)

**Rules for decomposition:**
- Start with foundational/context-setting questions that have no dependencies
- Build toward analytical/comparative questions that depend on foundational answers
- Aim for 4-8 nodes. If the topic needs more, cap at 12.
- Each node should be answerable with 1-3 web searches
- Questions should be specific enough that a researcher with no other context can answer them

**Print the DAG** as a table so the first brain can see the plan, then immediately proceed to execution, do not wait for confirmation.

**Create a task for each DAG node** using TaskCreate (description: the sub-question, status: pending). Also create tasks for "Gap analysis" and "Synthesize report". Update each task to `in_progress` when its wave launches and `completed` when the subagent returns. This gives the first brain real-time visibility into progress.

```
| ID | Question | Depends on |
|----|----------|------------|
| q1 | ...      |, |
| q2 | ...      |, |
| q3 | ...      | q1         |
| q4 | ...      | q1, q2     |
```

### 2. Execute in dependency order

The rest: https://github.com/rohitg00/awesome-claude-code-toolkit/blob/HEAD/skills/deep-dive/SKILL.md