OpenAI wants markdown structure. Anthropic prefers XML tags. Google emphasizes few-shot examples. So I built a simple agent system that reads the official prompting docs and applies them to the given prompt. Each optimizer runs a ReAct loop: - list_provider_docs → discover available guidelines - read_provider_doc → fetch specific content - submit_optimization → return structured result Agent decides what context it needs. The loop runs until submission: 1. LLM reasons about the task 2. Calls a tool 3. Observes result 4. Repeats Typically 3-4 iterations. Parallel execution via asyncio.gather; all providers optimize concurrently. Good to explore the agent design and prompts. Feel free to play with it. Shared the repo here: http://github.com/muratcankoylan/The-Rosetta-Prompt… @LangChainAI @OpenRouterAI