heady-coder

Generate and evaluate code using multi-model AI routing across MCP tools.

1|Updated Mar 24, 2026
One-click install
npx skills add https://github.com/HeadyAI/heady-context --skill heady-coder
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: heady-coder
Source: https://github.com/HeadyAI/heady-context/tree/main/heady-skills/heady-coder
Command: npx skills add https://github.com/HeadyAI/heady-context --skill heady-coder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables rapid, high-quality software code generation, evaluation, and development through a multi-model AI coding suite, pairing, and competitive routing.

Core Features & Use Cases

  • Multi-model code generation: route tasks to the best model for each coding task.
  • Battle-tested solutions: run parallel models and select top results via CSL scoring.
  • Pair programming: maintain context with a persistent AI buddy during development.
  • Model routing: auto-select and aggregate outputs from heady_coder, heady_battle, heady_buddy, and other MCP tools for optimal results.
  • Flexible development workflows: use for writing functions, building features, or creating software artifacts.

Quick Start

Provide a coding task such as "Implement a REST API endpoint for user signup" and let the system orchestrate across coder, battle, and buddy to produce the best solution.

Frequently Asked Questions about heady-coder

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

FAQPage Schema
How do I use multi-model AI to generate and evaluate code for a new feature?

Multi-model AI code generation routes your task to the best model, running parallel evaluations to select top results. You provide a coding task like building a REST API endpoint, and the system orchestrates across multiple tools to produce the optimal solution.

What is multi-model AI pair programming and how does it maintain context?

Multi-model AI pair programming uses a persistent AI buddy to maintain context during development. It interfaces with heady_buddy to keep track of your coding session, allowing continuous collaboration while writing functions or building software artifacts.

Can I benchmark different AI code generators against each other for the same task?

Yes, you can benchmark AI code generators using the battle-tested workflow. It runs parallel models on the same coding task and selects the top result using CSL scoring, allowing you to compare and evaluate code quality across different implementations.

Does the multi-model code routing workflow support debugging and building software artifacts?

The multi-model code routing workflow supports writing code, building features, debugging, and creating software artifacts. It auto-selects and aggregates outputs from multiple models to handle flexible development workflows across your coding tasks.

What's the best way to auto-select the right AI model for a specific coding task?

The best way to auto-select an AI model for coding is using the model routing workflow. It aggregates outputs from heady_coder, heady_battle, heady_buddy, and other tools, automatically routing tasks to the most suitable model for rapid, high-quality generation.