Agent Skillszereight/gitlab-mcp › deep-dive

deep-dive

GitHub

深度剖析技能,通过两阶段流水线(原因追踪与需求访谈)解决未知根因的问题。适用于Bug调查和功能探索,结合代码路径、配置等假设进行并行验证,最终输出包含追踪发现的完整规范文档。

.github/skills/deep-dive/SKILL.md zereight/gitlab-mcp

Trigger Scenarios

用户遇到未知根因的Bug需要调查 功能改进前需深入理解现状 用户输入 deep dive, investigate deeply 等指令

Install

npx skills add zereight/gitlab-mcp --skill deep-dive -g -y
More Options

Non-standard path

npx skills add https://github.com/zereight/gitlab-mcp/tree/main/.github/skills/deep-dive -g -y

Use without installing

npx skills use zereight/gitlab-mcp@deep-dive

指定 Agent (Claude Code)

npx skills add zereight/gitlab-mcp --skill deep-dive -a claude-code -g -y

安装 repo 全部 skill

npx skills add zereight/gitlab-mcp --all -g -y

预览 repo 内 skill

npx skills add zereight/gitlab-mcp --list

SKILL.md

Frontmatter
{
    "name": "deep-dive",
    "description": "2-stage pipeline: trace (causal investigation) then deep-interview (requirements). Activate when user says: deep dive, deep-dive, investigate deeply, trace and interview.\n",
    "argument-hint": "<problem or exploration target>"
}

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-diveralplan (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 .omc/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 .omc/specs/deep-dive-{slug}.md with additional "Trace Findings" section.

Version History

  • a8f5d4f Current 2026-07-25 10:03

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skills/gitlab-mcp/SKILL.md

Metadata

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Indexed
2026-07-25 10:03

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