AI SummaryA Windsurf cursor rules prompt that introduces the Lár framework—a graph-based agent architecture for building structured, type-safe code agents. Developers building multi-step AI workflows in Windsurf will find patterns for nodes, tools, and routers that enforce explicit typing and state management.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "DMN — Windsurf Rules" prompt rules to my project. Repository: https://github.com/snath-ai/DMN Please read the repo to find the rules/prompt file, then: 1. Download it to the correct location (.cursorrules, .windsurfrules, .github/prompts/, or project root — based on the file type) 2. If there's an existing rules file, merge the new rules in rather than overwriting 3. Confirm what was added
Description
Showcase of Cognitive Architectures: Replicating the Human Default Mode Network in Code.
Lár Framework - Cursor Rules
You are coding with Lár, a graph-based agent framework that treats "Code as the Graph".
Core Principles
• Strict Typing: Every Node and Function MUST have Pydantic/Python type hints. • Explicit Linking: Connect nodes using .next_node = target or RouterNode(path_map={...}). • No "Magic": Do not assume global state. Use state.get() and state.set().
1. Defining a Node
`python from lar import LLMNode architect = LLMNode( model_name="gemini/gemini-1.5-pro", prompt_template="Analyze {input}...", output_key="plan" ) `
2. Defining a Tool
`python from lar import ToolNode def my_tool(state): return "result" tool = ToolNode( tool_function=my_tool, input_keys=["__state__"], output_key="result", next_node=None ) `
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Works With
Any AI assistant that accepts custom rules or system prompts