deepdive

🥈Silver

Deepdive is a slash command for Claude Code that enables multi-round exploratory reasoning and non-linear problem-solving. It benefits operations teams by allowing iterative deep thinking for complex problem-solving. It connects to Claude Code for enhanced reasoning capabilities.

4930Updated 1mo ago

Intermediate30min to implementautomation

Saves ~105 min per use

Quick InstallView Source

git clone https://github.com/wanpengxie/deepdive.git

Works with:

Claude

Overview

About This Skill

How to Use

["Install the /deepdive slash command in your Claude Code environment by running `pip install claude-deepdive` and configuring your API key.","Start a deepdive session by typing `/deepdive` followed by your problem statement. Example: `/deepdive Why is our customer support ticket volume spiking in the EMEA region?`","Iterate by asking follow-up questions or requesting deeper analysis. Example: `Can you explore the correlation between ticket spikes and our recent pricing changes in EMEA?`","Use the output to refine your approach. If gaps remain, ask the AI to focus on specific areas (e.g., `Dig deeper into the technical root cause of the pricing tool bug`).","Export the final deepdive output to a document or ticketing system (e.g., Jira, Notion) for team review. Use the `/deepdive export` command to generate a shareable summary."]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/wanpengxie/deepdive

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use /deepdive to analyze [PROBLEM_STATEMENT] with iterative reasoning. Break the problem into sub-questions, explore edge cases, and validate assumptions. Iterate until you reach a well-reasoned conclusion or identify unresolved gaps. Start with: 'Let's deepdive into [PROBLEM_STATEMENT].'

Example Output

Let's deepdive into the root cause of the sudden 30% drop in user engagement for the mobile app in Q3 2024.

**Initial Analysis:**
The engagement drop correlates with the release of v2.4.1, which included a redesigned onboarding flow. However, the correlation isn't causal yet—we need to isolate variables.

**First Iteration:**
- Hypothesis 1: The new onboarding flow is too complex, causing users to abandon before completing setup.
- Hypothesis 2: A bug in v2.4.1 introduced performance issues, frustrating users.

**Second Iteration:**
- Checked crash reports: No spike in errors post-launch. Performance metrics (app load time, latency) remained stable.
- Analyzed funnel data: 40% of users who started onboarding in v2.4.1 dropped off at the 'permissions request' step, vs. 25% in v2.3.0.

**Third Iteration:**
- Surveyed 500 users: 68% cited 'too many permission requests upfront' as the reason for abandoning onboarding.
- Cross-referenced with A/B test data: Users who saw a simplified permissions flow (test group) had 15% higher completion rates.

**Conclusion:**
The root cause is the redesigned onboarding flow’s permission request strategy. The fix is to revert to the v2.3.0 permission flow and gradually reintroduce requests post-setup.

**Unresolved Gaps:**
- Need to validate if this is region-specific (e.g., GDPR-heavy regions may behave differently).
- Long-term impact of this change on user retention isn’t yet measurable (requires 30-day follow-up).