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Devils Advocate

  • 226 installs
  • 46 repo stars
  • Updated May 13, 2026
  • majesticlabs-dev/majestic-marketplace

Helps with ai & agent building tasks.

About

devils-advocate is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • devils-advocate
  • AI & Agent Building
  • AI-coding skill

Devils Advocate by the numbers

  • 226 all-time installs (skills.sh)
  • Ranked #2,687 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 1, 2026 (Skillselion catalog sync)
npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill devils-advocate

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Listed on Skillselion
Installs226
repo stars46
Last updatedMay 13, 2026
Repositorymajesticlabs-dev/majestic-marketplace

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

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 — includes an iteration gate that re-runs until no critical/major gaps remain (max 3 iterations)
  • devils-advocate: Pre-commitment challenge before reasoning solidifies
  • Use both: devils-advocate during planning, reasoning-verifier after execution
  • For iterative confidence building: run devils-advocate once, then hand the output to reasoning-verifier for gap-closing loops

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.

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