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adversarial-spec

zscole · zscole/adversarial-spec

Refines product specifications using a multi-LLM debate process where models critique and improve the spec until consensus is reached.

A Claude Code plugin that iteratively refines product specifications by debating between multiple LLMs until all models reach consensus.

556 stars50License: MITUpdated 9 months ago0 opens

Preview

▍

What you can do with it

  • Refine a product specification for a new rate limiter service through an automated multi-LLM debate.
  • Catch edge cases and inconsistencies in a feature requirement document by having multiple agents challenge it.
  • Generate a more robust and complete design document after it has passed rigorous adversarial review by diverse LLMs.

Safety check

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From the README

adversarial-spec

A Claude Code plugin that iteratively refines product specifications through multi-model debate until consensus is reached.

Key insight: A single LLM reviewing a spec will miss things. Multiple LLMs debating a spec will catch gaps, challenge assumptions, and surface edge cases that any one model would overlook. The result is a document that has survived rigorous adversarial review.

Claude is an active participant, not just an orchestrator. Claude provides independent critiques, challenges opponent models, and contributes substantive improvements alongside external models.

Quick Start

# 1. Add the marketplace and install the plugin
claude plugin marketplace add zscole/adversarial-spec
claude plugin install adversarial-spec

# 2. Set at least one API key
export OPENAI_API_KEY="sk-..."
# Or use OpenRouter for access to multiple providers with one key
export OPENROUTER_API_KEY="sk-or-..."

# 3. Run it
/adversarial-spec "Build a rate limiter service with Redis backend"

How It Works

You describe product --> Claude drafts spec --> Multiple LLMs critique in parallel
        |                                              |
        |                                              v
        |                              Claude synthesizes + adds own critique
        |                                              |
        |                                              v
        |                              Revise and repeat until ALL agree
        |                                              |
        +--------------------------------------------->|
                                                       v
                                            User review period
                                                       |
                                                       v
                                            Final document output
  1. Describe your product concept or provide an existing document
  2. (Optional) Start with an in-depth interview to capture requirements
  3. Claude drafts the initial document (PRD or tech spec)
  4. Document is sent to opponent models (GPT, Gemini, Grok, etc.) for parallel critique
  5. Claude provides independent critique alongside opponent feedback
  6. Claude synthesizes all feedback and revises
  7. Loop continues until ALL models AND Claude agree
  8. User review period: request changes or run additional cycles
  9. Final converged document is output

Requirements

  • Python 3.10+
  • litellm package: pip install litellm
  • API key for at least one LLM provider

Supported Models

ProviderEnv VarExample Models
OpenAIOPENAI_API_KEYgpt-4o, gpt-4-turbo, o1
AnthropicANTHROPIC_API_KEYclaude-sonnet-4-20250514, claude-opus-4-20250514
GoogleGEMINI_API_KEYgemini/gemini-2.0-flash, gemini/gemini-pro
xAIXAI_API_KEYxai/grok-3, xai/grok-beta
MistralMISTRAL_API_KEYmistral/mistral-large, mistral/codestral
GroqGROQ_API_KEYgroq/llama-3.3-70b-versatile
OpenRouterOPENROUTER_API_KEYopenrouter/openai/gpt-4o, openrouter/anthropic/claude-3.5-sonnet
Codex CLIChatGPT subscriptioncodex/gpt-5.2-codex, codex/gpt-5.1-codex-max
Gemini CLIGoogle accountgemini-cli/gemini-3-pro-preview, gemini-cli/gemini-3-flash-preview
DeepseekDEEPSEEK_API_KEYdeepseek/deepseek-chat
ZhipuZHIPUAI_API_KEYzhipu/glm-4, zhipu/glm-4-plus

Check which keys are configured:

python3 "$(find ~/.claude -name debate.py -path '*adversarial-spec*' 2>/dev/null | head -1)" providers

AWS Bedrock Support

For enterprise users who need to route all model calls through AWS Bedrock (e.g., for security compliance or inference gateway requirements):

# Enable Bedrock mode
python3 "$(find ~/.claude -name debate.py -path '*adversarial-spec*' 2>/dev/null | head -1)" bedrock enable --region us-east-1

# Add models enabled in your Bedrock account
python3 "$(find ~/.claude -name debate.py -path '*adversarial-spec*' 2>/dev/null | head -1)" bedrock add-model claude-3-sonnet
python3 "$(find ~/.claude -name debate.py -path '*adversarial-spec*' 2>/dev/null | head -1)" bedrock add-model claude-3-haiku

# Check configuration
python3 "$(find ~/.claude -name debate.py -path '*adversarial-spec*' 2>/dev/null | head -1)" bedrock status

# Disable Bedrock mode
python3 "$(find ~/.claude -name debate.py -path '*adversarial-spec*' 2>/dev/null | head -1)" bedrock disable

When Bedrock is enabled, all model calls route through Bedrock - no direct API calls are made. Use friendly names like claude-3-sonnet which are automatically mapped to Bedrock model IDs.

Configuration is stored at ~/.claude/adversarial-spec/config.json.

OpenRouter Support

OpenRouter provides unified access to multiple LLM providers through a single API. This is useful for:

  • Accessing models from multiple providers with one API key
  • Comparing models across different providers
  • Automatic fallback and load balancing
  • Cost optimization across providers

Setup:

# Get your API key from https://openrouter.ai/keys
export OPENROUTER_API_KEY="sk-or-..."

# Use OpenRouter models (prefix with openrouter/)
python3 debate.py critique --models openrouter/openai/gpt-4o,openrouter/anthropic/claude-3.5-sonnet < spec.md

Popular OpenRouter models:

  • openrouter/openai/gpt-4o - GPT-4o via OpenRouter
  • openrouter/anthropic/claude-3.5-sonnet - Claude 3.5 Sonnet
  • openrouter/google/gemini-2.0-flash - Gemini 2.0 Flash
  • openrouter/meta-llama/llama-3.3-70b-instruct - Llama 3.3 70B
View source on GitHub