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conjecture-criticism

Spawn parallel agents to critique competing approaches and build consensus.

3|1|Updated Mar 6, 2026
Search Tags:#decision making#parallel agents#evaluation#recommendation#critique#adversarial analysis
One-click install
npx skills add https://github.com/mrap/hexagon-base --skill conjecture-criticism
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: conjecture-criticism
Source: https://github.com/mrap/hexagon-base/tree/main/dot-claude/skills/conjecture-criticism
Command: npx skills add https://github.com/mrap/hexagon-base --skill conjecture-criticism

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill tackles the challenge of making well-reasoned recommendations by systematically identifying potential flaws and blind spots through adversarial thinking.

Core Features & Use Cases

  • Adversarial Analysis: Spawns parallel agents to generate competing approaches and critique each other.
  • Consensus Building: Facilitates the emergence of the strongest idea through cross-criticism.
  • Use Case: When deciding on a new software architecture, use this Skill to have different agents propose various designs, then have them rigorously critique each other's proposals to uncover hidden risks and trade-offs.

Quick Start

Run conjecture-criticism at moderate depth to evaluate the proposed architecture.

Frequently Asked Questions about conjecture-criticism

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is adversarial analysis for decision making?▼

Adversarial analysis for decision making spawns parallel agents to generate competing approaches and cross-critique each other. This mechanism builds consensus by exposing hidden risks and trade-offs before finalizing a recommendation.

How do I use parallel agents to evaluate software architecture options?▼

To evaluate software architecture options, spawn parallel agents to propose various designs, then execute structured critique protocols where they rigorously critique each other's proposals to uncover hidden risks and trade-offs.

Can I use agent-based simulation for strategic recommendations?▼

Yes, you can use agent-based simulation for strategic recommendations by spawning parallel agents to generate competing approaches and cross-critique each other, ensuring the strongest idea emerges through structured critique protocols.

When should I use cross-criticism for evaluating options?▼

Use cross-criticism for evaluating options when you need robust recommendations with real alternatives. It is particularly effective for identifying potential oversights and blind spots in strategic decisions like choosing a new software architecture.

What are the limitations of using parallel agents for critique?▼

A key limitation of using parallel agents for critique is the requirement for agent-based simulation and structured critique protocols. This approach adds computational overhead and is best suited for complex strategic decisions rather than simple evaluations.

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