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Portable, vendor-agnostic agent harness for project-specific skills, workflows, and agent teams aligned with your codebase, conventions, and engineering standards.

1,041 starsTypeScript

Oh-my-agent – A structural harness for AI agents in real projects

by otti-sister·Mar 17, 2026·3 points·1 comment

AI Analysis

●●SolidShip ItBig Brain

Portable .agents spec keeps skills separate from specific IDE plugins.

Strengths
  • Role-based agent teams enforce discipline over raw generation with specialized personas.
  • `.agents/` directory spec makes workflows portable across different AI IDEs.
Weaknesses
  • Agent orchestration is a saturated market with CrewAI, AutoGen, and LangGraph.
Target Audience

Engineering teams implementing AI agent swarms

Similar To

CrewAI · AutoGen · LangGraph

Post Description

If you ask an AI agent to build a todo app, it will usually produce something. But in a real development environment, limitations become obvious: hallucinations, drifting off task, or repeating the same mistakes.

Looking at current prompts and skills, a few recurring problems show up: missing critical library versions, vague personas like you are a senior engineer, and bloated prompts that burn tokens without improving outcomes.

oh-my-agent addresses this by introducing a structured protocol to keep agents aligned with real project constraints.

Key Ideas: 1. Clarification protocol: Analyzes requirement ambiguity to decide whether to proceed, offer options, or stop for clarification.

2. Difficulty guide: Adjusts protocol depth based on task complexity.

3. Context budget: Enforces token limits per model to prevent unnecessary usage.

OpenAI’s Symphony release this March reinforced the importance of this approach. Symphony is powerful for autonomous workflows, but it performs best when the codebase is designed to be AI-friendly.

OpenAI refers to this as harness engineering: structured documentation like ARCHITECTURE.md, AGENTS.md, and WORKFLOW.md that agents can reliably interpret. oh-my-agent is built around these principles. Instead of just sending prompts, it provides the structural harness agents need to understand project context and operate autonomously.

Currently supports: Antigravity, Claude Code, Codex CLI, and Cursor.

The core idea: Using AI agents effectively isn’t about clever prompts—it’s about the engineering structure around them.

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