deep-dive

Trace root causes across three parallel lanes and crystallize evidence-grounded requirements.

Updated Apr 17, 2026
One-click install
npx skills add https://github.com/dropsyoon/oh-my-claudecode --skill deep-dive-dropsyoon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-dive
Source: https://github.com/dropsyoon/oh-my-claudecode/tree/main/skills/deep-dive
Command: npx skills add https://github.com/dropsyoon/oh-my-claudecode --skill deep-dive-dropsyoon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deep-dive turns ambiguous problems into evidence-grounded requirements by investigating root causes before starting requirements definition.

Core Features & Use Cases

  • 3-lane causal tracing: Investigates WHY something happened using parallel hypotheses (code-path, config/orchestration, and measurement/artifact mismatch).
  • 3-point trace injection: Feeds the trace synthesis into the follow-up deep-interview so requirements crystallization starts with the right context instead of re-exploring.
  • Evidence-driven requirements: Produces a clear, actionable spec grounded in traced findings rather than assumptions.
  • Use case: When a production system behavior is unclear (or a bug feels intermittent), run deep-dive to determine the likely mechanism first, then generate the most appropriate fix requirements.

Quick Start

Run deep-dive with your problem statement, for example: /deep-dive "Why does the production DAG fail intermittently on the transformation step".

Frequently Asked Questions about deep-dive

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I investigate root causes before generating software requirements?▼

To investigate root causes before requirements generation, you can use causal tracing to analyze parallel hypotheses across code-path, configuration, and measurement lanes. This method translates ambiguous problems into evidence-grounded specifications by confirming the underlying mechanism first.

What is the best way to trace intermittent production bugs into actionable specs?▼

The best way to trace intermittent production bugs into actionable specs is running a 2-stage pipeline that couples causal tracing with requirements crystallization. It feeds synthesized trace evidence directly into a follow-up interview, ensuring the final spec is grounded in confirmed findings.

How does causal tracing work for ambiguous system behavior?▼

Causal tracing for ambiguous system behavior works by investigating three parallel hypotheses: code-path execution, config or orchestration mismatches, and measurement or artifact discrepancies. This multi-lane approach isolates the true mechanism causing the unclear production behavior.

Can I use pipeline automation to generate specs from bug investigation findings?▼

Yes, you can use pipeline automation to generate specs from bug investigation findings. The pipeline automates a 3-point trace injection that feeds confirmed causal hypotheses directly into requirements engineering, producing an actionable spec output ready for execution handoff.

Do I need to confirm hypotheses manually during root cause analysis?▼

Yes, you need to confirm hypotheses manually during root cause analysis. The pipeline requires user-confirmed hypotheses before proceeding to requirements crystallization, ensuring the generated specs rely on validated evidence rather than automated assumptions.

What are the limitations of generating specs without root cause discovery?▼

Generating specs without root cause discovery risks producing requirements based on assumptions rather than evidence. When production system behavior is unclear, skipping causal tracing and trace-to-interview injection often leads to ineffective fixes that fail to address the actual mechanism.