wei-devils-advocate（魏魔鬼代言人）

Wei Devil's Advocate Skill

Version: 1.0.0 | Last updated: 2026-04-07

Overview

Use wei-devils-advocate to stress-test ideas through multi-LLM adversarial cross-validation.

Multiple language models independently act as devil’s advocates, challenging the idea from different reasoning paths to uncover hidden risks, assumptions, and failure modes that a single model may miss.

It is best suited for:

Identifying hidden assumptions through cross-model disagreement Exposing risks, edge cases, and failure scenarios Detecting overconfident or internally consistent but fragile reasoning Validating decisions under adversarial multi-perspective review

Do NOT use this skill if you are looking for validation, consensus， quick agreement, brainstorming, or single-perspective answers.

Installation

Prerequisites

• Bun runtime (v1.0.0 or higher)

Install Bun

curl -fsSL https://bun.sh/install | bash

Or on macOS with Homebrew:

brew install oven-sh/bun/bun

Install Dependencies

cd skills/wei-devils-advocate
bun install

Environment Setup

Create a .env file in the skill directory with your API keys:

# Required: OpenRouter API key (for debater models)
OPENROUTER_API_KEY=your_openrouter_api_key_here

# Required: DashScope/Bailian API key (for judge model)
DASHSCOPE_API_KEY=your_dashscope_api_key_here

Or export as environment variables:

export OPENROUTER_API_KEY=your_openrouter_api_key_here
export DASHSCOPE_API_KEY=your_dashscope_api_key_here

Note: If environment variables are not set, the skill will throw an error with instructions on how to configure them.

Configuration Files

遇到模型访问问题？ 请参考 README.md 了解如何根据你的网络环境选择和配置 config.json。

Core Philosophy

Most bad decisions don’t fail because of lack of information.

They fail because:

• Assumptions go unchallenged
• Risks are underestimated
• Everyone agrees too quickly

This skill enforces:

“Default to skepticism. Earn confidence.”

How It Works

User Input (Thesis / Idea) ↓ [Debater Models x N] → Generate strongest counterarguments ↓ (Optional) [Simulation Models] → Attempt to rebut critiques multiple rounds until... ↓ [Judge Model] → Evaluates survivability ↓ Structured Decision Output

Modes

Note: Currently only the attack mode is implemented. Future versions include the simulation mode for simulating whether an idea survives sustained attack. Preview the 'simulation' mode at https://www.bigbigai.com/agent/devils-advocate .

Use Cases

• Product & Startup validation
• Investment / trading risk analysis
• Strategy stress testing
• System / prompt failure analysis

Cost Note

Uses multiple models (2–4x cost vs single query). Use for high-stakes decisions only.

Model Roles

Each model in config.json is tagged with one or more roles indicating its capabilities:

Note: Specific model names and their roles are defined in config.json → models. Refer to that file for the current model roster.

Model Selection

Model selection is controlled via config.json using a queryType-based routing system. Instead of hard-coding model names, you select models by the domain of the query.

How to Select Models

As the calling model, follow this process:

1. Classify the query — Match keywords to determine the queryType
2. Pass queryType — The skill will look up the routing.xxx.models in config.json
3. (Optional) Pass explicit models — Use the models parameter to bypass auto-selection

Query Types (Domain)

Selection Algorithm

1. Analyze query → match keywords → determine queryType
2. Pass queryType to skill → skill looks up `routing.<queryType>.models` in config.json
3. Skill selects top 2–3 models from the routing config
4. Debater models generate counterarguments
5. Judge model evaluates survivability

Examples

Example 1: Financial Query

Query: "Should we invest in AI startups in 2026?"

Selection process:

1. Keywords: invest, startups, 2026 → queryType: financial
2. Pass to skill: { "query": "...", "queryType": "financial" }
3. Skill looks up: config.json → routing.financial.models
4. Skill selects: Models configured for financial analysis
5. Judge: Evaluates investment thesis survivability

Example 2: Technical Query

Query: "Is microservices architecture the right choice for our startup?"

Selection process:

1. Keywords: microservices, architecture → queryType: technical
2. Pass to skill: { "query": "...", "queryType": "technical" }
3. Skill looks up: config.json → routing.technical.models
4. Skill selects: Models with technical/coding roles

Example 3: Product Validation

Query: "Will users pay for this productivity app?"

Selection process:

1. Keywords: users, pay, app → queryType: social
2. Pass to skill: { "query": "...", "queryType": "social" }
3. Skill looks up: config.json → routing.social.models
4. Skill selects: Models with social/retrieval roles

Skill Parameters

• query (string)
• queryType (string)
• intent (string)
• mode (string)
• models (array)
• maxModels (number)
• judgeModel (string)

Output Structure

1. Thesis
2. Hidden Assumptions
3. Counterarguments
4. Failure Scenarios
5. Survivability
6. Verdict
7. Recommendation

Tagline

Strong ideas survive attack. Weak ones don’t.