deep-dive

Run a two-stage pipeline for root-cause investigation and requirements crystallization.

Updated May 20, 2026
One-click install
npx skills add https://github.com/xdkp/oh-my-claudecode --skill deep-dive-xdkp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-dive
Source: https://github.com/xdkp/oh-my-claudecode/tree/main/skills/deep-dive
Command: npx skills add https://github.com/xdkp/oh-my-claudecode --skill deep-dive-xdkp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deep-dive helps you investigate why something happened and immediately turn the evidence into clear, actionable requirements instead of losing context between separate trace and interview steps.

Core Features & Use Cases

  • Two-stage causal pipeline: runs a 3-lane trace (implementation, config/orchestration, and measurement/assumptions) and then uses the findings for requirements crystallization.
  • 3-point trace injection: enriches the initial problem framing, seeds codebase/system context, and provides an initial question queue derived from unresolved critical unknowns.
  • Resume-ready artifacts: saves trace and final spec outputs to persistent paths for robust continuation.
  • Best-practice handoff: produces a spec that can be forwarded into consensus planning and automated execution.

Quick Start

Use the deep-dive skill when you need to investigate the root cause first, then get a precise plan for what to do next by running: /deep-dive "Why does the production pipeline 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 trace root causes of intermittent bugs and turn them into requirements?▼

Root cause tracing and requirements crystallization are handled as a two-stage pipeline that runs multi-lane causal tracing and then injects the findings into a targeted interview to produce an actionable spec.

What is the best way to investigate ambiguous production pipeline failures?▼

Investigating ambiguous failures requires running a 3-lane trace across implementation, config orchestration, and measurement assumptions, then gating progression until ambiguity is low enough for spec generation.

How does multi-lane causal tracing work for complex debugging?▼

Multi-lane causal tracing works by splitting evidence-based investigation into implementation, config orchestration, and measurement lanes, then seeding codebase context and an interview question queue from unresolved unknowns.

Can I use trace findings to initialize a requirements interview without data injection risks?▼

Trace findings enrich problem framing and interview initialization with safeguards against treating data as instructions, ensuring evidence context carries forward safely during requirements crystallization.

How do I resume a root cause analysis session after losing context?▼

Resuming root cause analysis relies on resume-ready artifacts that save trace outputs and final specs to persistent paths, allowing robust continuation of the investigation pipeline.

When should I not use a two-stage trace and interview pipeline?▼

A two-stage trace and interview pipeline should not be used when a problem is straightforward enough to skip evidence-based investigation or when immediate spec generation is required without ambiguity reduction.