Why should I keep my AGENTS.md file under 100 lines?

Brevity is key for AI agents; keeping instructions concise prevents context dilution and ensures the agent prioritizes the most critical project rules and commands.

What is an agentic loop?

An agentic loop is a workflow where the AI agent uses provided verification tools—like linters, type checkers, and test runners—to autonomously validate and fix its own work.

When should I move instructions to task-specific files?

You should move detailed guidelines to files like ARCHITECTURE.md or TESTING.md when your main AGENTS.md exceeds 100 lines or contains complex, domain-specific rules.

How does the Gotchas Codex prevent errors?

The Gotchas Codex documents real-world failures and nuances specific to your codebase, giving the agent a historical reference to avoid repeating past mistakes.

Agent Instruction Architect

Name: Agent Instruction Architect
Author: lhohan

bylhohan

Herramientas para Desarrolladores

Creates and maintains optimized AGENTS.md files to improve AI coding agent performance and reliability.

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This skill streamlines the creation of AGENTS.md instruction files, ensuring they adhere to industry best practices for AI agent orchestration. It helps developers establish clear project overviews, define verification tools for agentic loops, and maintain a 'Gotchas Codex' to prevent recurring mistakes. By focusing on brevity and modular documentation, it reduces context bloat while providing AI agents with the precise guidance needed to operate autonomously and accurately within any codebase.

Características Principales

Modular task-specific documentation referencing
Agentic loop verification tool configuration
Automated AGENTS.md skeleton generation
Living Gotchas Codex for error prevention
0 GitHub stars
OpenAI-aligned instruction optimization

Casos de Uso

Refining agent instructions to reduce coding errors
Setting up a new repository for AI-assisted development
Scaling documentation for complex projects without context bloat

Acerca de

Características Principales

Modular task-specific documentation referencing
Agentic loop verification tool configuration
Automated AGENTS.md skeleton generation
Living Gotchas Codex for error prevention
0 GitHub stars
OpenAI-aligned instruction optimization

Casos de Uso

Refining agent instructions to reduce coding errors
Setting up a new repository for AI-assisted development
Scaling documentation for complex projects without context bloat