deep-dive

Run a two-stage trace-and-requirements pipeline to resolve unclear root causes.

Updated Feb 23, 2026
One-click install
npx skills add https://github.com/cheafi/Trading-bot-CC --skill deep-dive-cheafi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-dive
Source: https://github.com/cheafi/Trading-bot-CC/tree/main/.github/skills/deep-dive
Command: npx skills add https://github.com/cheafi/Trading-bot-CC --skill deep-dive-cheafi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you identify why something happened when the root cause is unclear, then turn those findings into actionable requirements.

Core Features & Use Cases

  • Two-stage investigation and requirements pipeline: runs a causal trace first, then a deep-interview to define what to do based on the trace.
  • Three-lane trace with confirmation: generates and presents three hypotheses (code-path, config/env, measurement/artifact) for a single confirmation round, then executes three parallel tracer lanes.
  • Trace-to-interview injection: injects trace-derived likely explanations, replaces codebase context with synthesized results, and prioritizes questions from per-lane critical unknowns (or defers conclusions when low confidence).
  • Execution bridge options: hands off to downstream execution/consensus workflows like ralplan or omg-autopilot.

Quick Start

Ask: "deep dive <problem or exploration target>".

Frequently Asked Questions about deep-dive

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

FAQPage Schema
How do I find the root cause of a system failure when the evidence is unclear?▼

To find the root cause of unclear system failures, deep-dive runs a two-stage trace-and-requirements pipeline that generates three hypotheses across code-path, config, and artifact lanes for parallel investigation.

What is the best way to turn debugging findings into actionable requirements?▼

Turning debugging findings into actionable requirements involves injecting trace-derived explanations into a deep-interview workflow, prioritizing questions from per-lane critical unknowns to define next steps.

How do I diagnose broken systems using hypothesis tracing?▼

Diagnosing broken systems with hypothesis tracing works by presenting three causal hypotheses for a single confirmation round, then executing three parallel tracer lanes to produce trace evidence.

Can I hand off codebase investigation results to downstream pipeline orchestration?▼

Yes, you can hand off codebase investigation results to downstream pipeline orchestration tools like ralplan or omg-autopilot through available execution bridge options after synthesizing the trace.

What happens when root cause analysis has low confidence in critical unknowns?▼

When root cause analysis encounters low confidence in critical unknowns, the deep-interview workflow defers conclusions and replaces codebase context with synthesized results to avoid inaccurate requirements.