mordecai 16892434

Cursor AI Prompting Framework — Usage Guide

A disciplined, evidence-first workflow for autonomous code agents

1 · Install the Operational Doctrine

The Cursor Operational Doctrine (file core.md) encodes the agent’s always-on principles—reconnaissance before action, empirical validation over conjecture, strict command-execution hygiene, and zero-assumption stewardship.

title

Creating Common Webpage Layouts With TailwindCSS Using CSS Grid & CSS Flex box (w/ Code Examples)

CSS Grid: for the main page layout (header, main content, sidebar, footer)
CSS Flexbox: for smaller components within those grid areas (like navigation menus, lists of items, or aligning content within a section)

[Project Name]

Every time you choose to apply a rule(s), explicitly state the rule(s) in the output. You can abbreviate the rule description to a single word or phrase.

Project Context

[Brief description ]

[more description]
[more description]
[more description]

Education App Project Blueprint

1. Project Overview

Our project aims to develop a sophisticated education app that provides a hyper-personalized learning experience to help individuals maximize their educational potential. The app will assist users in understanding complex topics, identifying knowledge gaps, recognizing learning opportunities, and preparing for exams or presentations.

2. Key Features

2.1 Interactive Chat

Frontend Development Assistant

You are an expert frontend developer specializing in modern web development. Your primary focus is on crafting clean, maintainable solutions using the following technology stack:

Technology Stack

Core Frameworks

React 18+ with TypeScript
Vue 3+ with TypeScript (Composition API preferred)

People

`:bowtie:`	😄 `:smile:`	😆 `:laughing:`
😊 `:blush:`	😃 `:smiley:`	☺️ `:relaxed:`
😏 `:smirk:`	😍 `:heart_eyes:`	😘 `:kissing_heart:`
😚 `:kissing_closed_eyes:`	😳 `:flushed:`	😌 `:relieved:`
😆 `:satisfied:`	😁 `:grin:`	😉 `:wink:`
😜 `:stuck_out_tongue_winking_eye:`	😝 `:stuck_out_tongue_closed_eyes:`	😀 `:grinning:`
😗 `:kissing:`	😙 `:kissing_smiling_eyes:`	😛 `:stuck_out_tongue:`

	from PIL import Image
	import stable_inference
	import torch
	from einops import repeat

	def image_grid(imgs, rows, cols):
	assert len(imgs) == rows*cols

	w, h = imgs[0].size
	grid = Image.new('RGB', size=(colsw, rowsh))

	from PIL import Image
	import stable_inference
	import numpy as np
	from einops import repeat

	# Interpolate video between two conditionings
	FOLDER = 'test/painting'
	MAX_STRENGTH = 0.5 # Strength at maximum in the middle of the interpolation
	SEED = 9001
	SECONDS = 10

	import inspect
	from typing import Any, Callable, Dict, List, Optional, Union

	import numpy as np
	import torch
	from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast

	from diffusers.image_processor import VaeImageProcessor
	from diffusers.loaders import FluxLoraLoaderMixin
	from diffusers.models.autoencoders import AutoencoderKL