deep-divelisted

# 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, statu