adversarial-spec

🥈Silver

Adversarial-spec is a Claude Code plugin that uses multiple LLMs to debate and refine product specifications. It helps product and engineering teams identify gaps, challenge assumptions, and improve spec quality. The plugin integrates with Claude Code and connects to product management workflows, enhancing collaboration and reducing errors in specifications.

465410Updated 1w ago

Intermediate30min to implementautomation

Saves ~210 min per use

Quick InstallView Source

git clone https://github.com/zscole/adversarial-spec.git

Works with:

Claude

Overview

About This Skill

https://github.com/zscole/adversarial-spec

Use Cases

Automate the generation of product requirement documents (PRDs) by refining initial drafts through model debates.

Enhance technical specifications by identifying and addressing potential gaps and assumptions through multi-LLM critiques.

Facilitate collaborative product development by synthesizing feedback from various LLMs into a final document.

Streamline the review process by allowing users to request changes and run additional critique cycles until consensus is reached.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/zscole/adversarial-spec

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Refine the product specifications for [PRODUCT NAME] by debating between multiple LLMs. Start with the initial specifications: [INITIAL SPECIFICATIONS]. Generate arguments for and against each specification and reach a consensus on the final product specs. Ensure the final output is clear and actionable.

Example Output

After a thorough debate among the LLMs regarding the specifications for the new smartwatch, the following consensus was reached:

1. **Battery Life**: Initial spec suggested 24 hours. Argument against was that competitors offer 48 hours. Consensus is to aim for 36 hours, balancing performance and user expectations.

2. **Water Resistance**: Initial spec stated IP67 rating. Debate highlighted that users prefer IP68 for better usability in diverse conditions. Final consensus is to enhance to IP68.

3. **Health Monitoring Features**: Initial spec included heart rate monitoring only. Arguments for expanding to include blood oxygen and sleep tracking were compelling. Final specs now include heart rate, blood oxygen, and sleep tracking.

4. **Price Point**: Initial suggestion was $199. Counterarguments indicated that similar products are priced at $249. The final consensus is to set the price at $229, positioning it competitively while reflecting the enhanced features.

The final refined specifications for the smartwatch are now: 36-hour battery life, IP68 water resistance, comprehensive health monitoring (heart rate, blood oxygen, sleep tracking), and a price of $229.