Layers

AI conventions are easier to reason about when grouped by the role they play in an agent workflow. Use this map to decide which artifact belongs in a repository, docs site, or integration surface.

Quick Decision Map

Need	Start with	Why
Tell coding agents how to work in a repository	AGENTS.md	Cross-tool project instructions are the highest-leverage baseline.
Preserve long-lived project context	MEMORY.md or Memory Bank	These files keep stable knowledge and task state outside a single chat.
Package a reusable agent capability	SKILL.md	Skills are loaded on demand when a task matches the description.
Store prompts as versioned assets	.prompty, .prompt, or system_prompt.txt	Prompt files keep model instructions inspectable and reviewable.
Make behavior measurable	EVAL.yaml	Evals turn agent quality into a repeatable check.
Help LLMs discover public docs	llms.txt and llms-full.txt	Predictable Markdown entry points reduce scraping ambiguity.
Connect models to tools or agents	MCP, A2A, Agent Cards	Protocols define interoperability beyond one repository.

Instruction Layer

These files tell agents how to behave in a repository.

• AGENTS.md
• CLAUDE.md
• Tool-specific rules such as Cursor rules, Cline rules, and Copilot instructions
• .aiignore

Context and State Layer

These files preserve project knowledge, task state, or execution intent.

• MEMORY.md
• Memory Bank folders such as cline_docs/ and .roo/
• PLAN.md

Prompt and Capability Layer

These files package model instructions and agent capabilities into reusable assets.

• .prompty
• .prompt
• system_prompt.txt
• SKILL.md

Evaluation Layer

These files make behavior testable and repeatable.

• EVAL.yaml

Discoverability Layer

These files expose structured information to LLMs and API-aware clients.

• llms.txt
• llms-full.txt
• pricing.md
• auth.md
• /.well-known/ai-plugin.json

Protocol Layer

These standards define interoperability between tools, models, and agents.

• Model Context Protocol
• Agent Cards / A2A