deep-dive

Featured

2-stage pipeline: trace (causal investigation) -> deep-interview (requirements crystallization) with 3-point injection

AI & Automation 39,098 stars 3499 forks Updated today MIT

Install

View on GitHub

Quality Score: 99/100

Stars 20%
100
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

<Purpose> Deep Dive orchestrates a 2-stage pipeline that first investigates WHY something happened (trace) then precisely defines WHAT to do about it (deep-interview). The trace stage runs 3 parallel causal investigation lanes, and its findings feed into the interview stage via a 3-point injection mechanism — enriching the starting point, providing system context, and seeding initial questions. The result is a crystal-clear spec grounded in evidence, not assumptions. </Purpose> <Use_When> - User has a problem but doesn't know the root cause — needs investigation before requirements - User says "deep dive", "deep-dive", "investigate deeply", "trace and interview" - User wants to understand existing system behavior before defining changes - Bug investigation: "Something broke and I need to figure out why, then plan the fix" - Feature exploration: "I want to improve X but first need to understand how it currently works" - The problem is ambiguous, causal, and evidence-heavy — jumping to code would waste cycles </Use_When> <Do_Not_Use_When> - User already knows the root cause and just needs requirements gathering — use `/deep-interview` directly - User has a clear, specific request with file paths and function names — execute directly - User wants to trace/investigate but NOT define requirements afterward — use `/trace` directly - User already has a PRD or spec — use `/ralph` or `/autopilot` with that plan - User says "just do it" or "skip the investigation" — respect their int...

Details

Author
Yeachan-Heo
Repository
Yeachan-Heo/oh-my-claudecode
Created
8 months ago
Last Updated
today
Language
TypeScript
License
MIT

Bundled in these plugins

Similar Skills

Semantically similar based on skill content — not just same category

AI & Automation Listed

deep-dive

Rigorous multi-agent deep-dive analysis for complex investigative tasks — auditing codebases, evaluating strategies or systems, validating designs, doing open-ended research. Deploys 4–6 specialist agents in parallel across distinct lanes, then synthesis, then adversarial red-team review, then optional patching — producing structured markdown research files plus a plain-English executive briefing with honest 1–10 confidence ratings. ALWAYS invoke when the user says any of "deep dive", "thorough audit", "rigorous analysis", "comprehensive review", "audit this codebase", "analyze the strategy", "evaluate this design", "review this thoroughly", or "research deep dive". Also invoke proactively for any open-ended investigative task involving a codebase, strategy, system design, or research question that warrants 30+ minutes of structured analysis — even when the user doesn't use these exact phrases.

83 Updated 1 months ago
nelsonwerd
AI & Automation Listed

deep-dive

Deep technical investigation for complex systems, unfamiliar codebases, architecture, data flows, and dependencies - when the system works but is not understood. Use when the user asks for a deep dive, investigate deeply, map the system, understand how it works, break down complexity, or produce an evidence-backed technical brief. If something is failing right now use the troubleshooting reference in morpheus; for a past incident timeline and contributing factors use premortem-postmortem.

1 Updated yesterday
CassioRoos
AI & Automation Listed

deep-dive

Use when told "do a deep dive", "go figure this out", or "don't reinvent the wheel" - researches a question across every relevant source and returns a decision. Also fires on "see what everyone else is doing", "use all the skills you have", "what's the best way to X", "should we build this or buy it", "is this possible", "what is everyone doing about X", "research this properly", and any consequential question handed over for a researched answer rather than a quick one. Routes one question across the relevant source classes (primary docs and source code, open-source repos, commercial vendors, practitioner community, academic work, prediction markets, and the operator's own prior work), verifies decision-critical claims against the PING failure modes, and returns a decision-first brief, with an explicit build-vs-adopt call when the question is a sourcing decision. Starts at the cheapest route that can settle the question and escalates only against a named gap.

0 Updated 2 days ago
TechNickAI