What problem does it solve? When building multiple Agno agents, inconsistent folder layouts and ad-hoc configuration make projects hard to navigate, review, and reuse. This Skill provides a canonical project structure so every agent follows the same predictable organization. ## Core Features & Use Cases - Standard Agent Layout: Defines the directory structure for each agent (pyproject.toml, main.py, config.py, prompts/, tests/) with isolated dependencies managed via uv. - Model Factory Pattern: Provides a reusable config.py that centralizes provider selection (OpenAI, Groq, Ollama) through environment variables, keeping examples model-agnostic. - Prompt Management via S3: Establishes prompts/system_prompt.md as the canonical source, synced to an S3 bucket with local fallback so agents never start without a prompt. - Use Case: When creating a new study agent, apply this Skill to scaffold the folder layout, entry point, README requirements, and configuration files consistently with the rest of the workspace. ## Quick Start Ask the AI to create a new Agno agent following the standard project structure with a main.py entry point, config.py model factory, and prompts folder.
