dev-ask

Route prompts to Claude, Codex, or Gemini for second opinions.

520|175|Updated Apr 8, 2026
One-click install
npx skills add https://github.com/EvolutionAPI/evo-nexus --skill dev-ask-evolutionapi
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dev-ask
Source: https://github.com/EvolutionAPI/evo-nexus/tree/main/.claude/skills/dev-ask
Command: npx skills add https://github.com/EvolutionAPI/evo-nexus --skill dev-ask-evolutionapi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Advisory router to obtain a fast, model-specific second opinion on prompts, enabling quick cross-model validation for engineering tasks.

Core Features & Use Cases

  • Single-model second-opinion workflow: choose a target model and submit a prompt to receive a succinct critique.
  • Experimental status awareness: provides guidance when a model isn't configured and suggests alternatives or fallbacks.
  • Output traceability: supports saving/recording opinions to workspace for comparison and auditing.

Quick Start

Ask the advisor to provide a quick second opinion from Claude, Codex, or Gemini on your prompt.

Frequently Asked Questions about dev-ask

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

FAQPage Schema
How do I get a second opinion on a prompt from different AI models?

To get a second opinion on a prompt, submit it to the advisory router to receive a fast, model-targeted critique from Claude, Codex, or Gemini. This allows quick cross-model validation for engineering tasks like prompt validation and design reviews.

Can I use cross-model validation for quick feasibility checks during engineering workflows?

Yes, you can use cross-model validation for quick feasibility checks within engineering workflows. The advisor provides succinct critiques from a chosen target model, helping you validate designs and prompts efficiently before full implementation.

What happens if a target model like Codex or Gemini is not configured?

If a target model is not configured, the advisory router provides fallback guidance. It returns clearly labeled results with experimental status awareness and suggests available alternatives or fallback models to ensure your workflow continues.

How do I save model critiques for later comparison and auditing?

You can save model critiques for comparison and auditing by using the output traceability feature. This supports saving and recording the second opinions directly to your workspace, enabling easy review of cross-model validation results over time.