搜索

PythonReactSEOPDF
GitHub Skill

devils-advocate

Forces adversarial reasoning before committing to decisions. Triggers on architectural choices, approach selection, and planning phases to prevent premature commitment bias.

majesticlabs-dev☆ 20 仓库 Stars3 仓库 Forks

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

查看来源 ↗使用指南

技能说明

Devil's Advocate Protocol

Pre-commitment adversarial reasoning to prevent early lock-in and expose blind spots.

When to Apply

Activate this protocol when:

  • Choosing between architectural approaches
  • Selecting libraries, frameworks, or tools
  • Planning implementation strategy
  • Recommending one approach over alternatives
  • User asks "should I...", "what's the best way to...", "which approach..."
  • During architect, Plan, or blueprint workflows
  • Making trade-off decisions with non-obvious answers

When to Skip

Do NOT apply when:

  • Executing already-decided implementation
  • Single obvious path exists (no real alternatives)
  • User explicitly chose the approach ("use X to do Y")
  • Task is mechanical/procedural, not decisional
  • Trivial choices with negligible impact

The Protocol

Step 1: Identify the Commitment

Before recommending an approach, explicitly state:

  • What decision is being made
  • What approach you're inclined toward
  • Why you're drawn to it
Step 2: Steel-Man the Opposition

Present the strongest case AGAINST your inclination:

  • What could go wrong?
  • What are you assuming that might be false?
  • What would a smart critic say?
  • What's the opportunity cost?
  • Under what conditions would this fail?

Requirements:

  • Be genuinely adversarial, not token objections
  • Attack the strongest version of your argument
  • Include at least one non-obvious failure mode
Step 3: Defend or Pivot

After the adversarial pass:

  • Explain why the approach might still be correct despite objections
  • What conditions make this the right choice?
  • What would need to be true for alternatives to win?
  • OR: Acknowledge the objections changed your recommendation
Step 4: Present with Confidence Calibration

Final recommendation should include:

  • Clear recommendation with reasoning
  • Key assumptions that must hold
  • Conditions that would invalidate this choice
  • Monitoring signals to watch for

Output Format

## Decision: [What's being decided]

### Initial Inclination
[Approach] because [reasons]

### Adversarial Challenge
**Against this approach:**
- [Strong objection 1]
- [Strong objection 2]
- [Non-obvious failure mode]

**What I might be wrong about:**
- [Assumption that could be false]

### Resolution
[Why it's still correct OR why I'm changing recommendation]

### Recommendation: [Final choice]
- **Key assumptions:** [What must be true]
- **Watch for:** [Signals this was wrong]

Relationship to Other Tools

  • reasoning-verifier: Post-hoc verification of completed reasoning
  • devils-advocate: Pre-commitment challenge before reasoning solidifies
  • Use both: devils-advocate during planning, reasoning-verifier after execution

Underlying Principle

LLMs commit to answers early and rationalize backward. This protocol interrupts that pattern by forcing exploration of the solution space before commitment crystallizes.

使用示例

查看来源仓库中的使用示例与配置说明。

查看来源 ↗

来源历史

收录时间
2026-01-20
索引更新时间
2026-02-04
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

GitHub ↗

chat Comments (0)

chat_bubble_outline

No comments yet. Be the first to share your thoughts!