jmstar85/oh-my-githubcopilot/deep-dive

Deep Dive

Orchestrates a 2-stage pipeline: first investigate WHY something happened (trace), then define WHAT to do about it (deep-interview). Trace findings feed into the interview via 3-point injection.

Pipeline

deep-dive → ralplan (consensus refinement) → omg-autopilot (execution)

When to Use

• User has a problem but doesn't know the root cause
• Bug investigation: "Something broke and I need to figure out why"
• Feature exploration: "I want to improve X but first need to understand it"

When NOT to Use

• Already know the root cause → use /deep-interview
• Clear specific request → execute directly
• Investigation only, no requirements → use /trace

Phases

Phase 1: Initialize

1. Parse problem, detect brownfield/greenfield
2. Generate 3 trace lane hypotheses (code-path, config/env, measurement/artifact)

Phase 2: Lane Confirmation

Present hypotheses to user for confirmation (1 round).

Phase 3: Trace Execution

Run 3 parallel tracer lanes using @tracer agents:

• Each lane: evidence for, evidence against, critical unknown, discriminating probe
• Rebuttal round between top hypotheses
• Convergence detection
• Save to .omg/specs/deep-dive-trace-{slug}.md

Phase 4: Interview with Trace Injection

Follow deep-interview protocol with 3 overrides:

1. initial_idea enrichment: Include trace's most likely explanation
2. codebase_context replacement: Use trace synthesis (skip re-exploration)
3. question queue injection: Per-lane critical unknowns become first questions

Low-confidence trace: don't inject uncertain conclusion, use ALL unknowns as questions.

Phase 5: Execution Bridge

Same options as deep-interview: ralplan → omg-autopilot (recommended), omg-autopilot, ralph, team, or refine further.

Output

Spec saved to .omg/specs/deep-dive-{slug}.md with additional "Trace Findings" section.