zereight 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
- Parse problem, detect brownfield/greenfield
- 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
.omc/specs/deep-dive-trace-{slug}.md
Phase 4: Interview with Trace Injection
Follow deep-interview protocol with 3 overrides:
- initial_idea enrichment: Include trace's most likely explanation
- codebase_context replacement: Use trace synthesis (skip re-exploration)
- 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 .omc/specs/deep-dive-{slug}.md with additional "Trace Findings" section.