Dumky de Wilde dumkydewilde

Mac Installation Script

Opinionated new Mac installation script — terminal settings, apps, git config and more (VS Code, iTerm2, Atuin, uv)

Instructions

Download the files
Adjust the brew.txt and brew_cask.txt to your liking
Open the folder with the files in the terminal
bash install.sh
Get coffee ☕️

name: web-scraper description: > Build reliable, production-grade web scrapers through a two-phase approach: Discovery (finding the best data source on a site) then Scraping (generating a Python script for scheduled extraction). Use this skill whenever the user wants to scrape a website, extract data from web pages, build a crawler, set up recurring data collection, reverse-engineer a site's API, or find hidden data endpoints. Also trigger when the user mentions "scrape", "crawl", "extract from site", "web data", "pull data from URL", "scheduled scraping",

dbt City: SimCity-style dbt Lineage Viewer

dbt City renders your dbt project's DAG as an isometric SimCity-style city. Sources become factories, models become office buildings (sized by connectivity), seeds become parks, and snapshots become warehouses. Schema groups form neighborhoods connected by a street grid that represents data lineage.

Quick Start

Open index.html in any modern browser. That is it: no server, no dependencies, no build step. The included example uses the classic jaffle_shop project.

	const NONCE_SECRET = "S3CR3T" // Identify where to inject nonces in your page

	class ElementHandler {
	constructor(generatedNonce, nonceReplaceValue) {
	this.generatedNonce = generatedNonce
	this.nonceReplaceValue = nonceReplaceValue
	}

	element(element) {
	// An incoming element, such as `div`

	import requests, json, ipaddress

	def get_cidr_ranges(provider):
	if provider == 'aws':
	url = 'https://ip-ranges.amazonaws.com/ip-ranges.json'
	base_key = 'prefixes'
	key = 'ip_prefix'
	elif provider == 'gcp':
	url = 'https://www.gstatic.com/ipranges/cloud.json'
	base_key = 'prefixes'

	# You can run this script to generate the SQL files for the models and fetch columns names from Databricks.
	# Run it on the command line, like this:
	# python utils/generate_models.py \
	# -yml models/staging/salesforce/_source_salesforce.yml \
	# -o models/staging/salesforce
	# Or see all options with python utils/generate_models.py --help

	import yaml
	import os
	import requests

	{% materialization streamlit, adapter='snowflake', supported_languages = ['python'] %}

	{% set original_query_tag = set_query_tag() %}

	{% set identifier = model['alias'] %}

	{% set app_name = config.get('app_name', identifier) %}

	{% set stage_name = config.get('stage', 'streamlit_apps') %}
	{% set create_stage = config.get('create_stage', true) %}

	system: >
	You are BuddAE, an experienced data and analytics engineer with a thorough understanding of analytics
	and data modelling. You are sceptical and cynical about sales, vendors and
	AI hype, but you are excited about the future.

	Think: Stephen Fry meets Claude Shannon. You have a preference for concise and to the point responses.
	First: try to share your assumptions or give different options when there is no clear
	'standard' way of doing things. Otherwise ask a clarifying question.
	- Your preferred stack is Python (with uv), dbt, DuckDB, BigQuery, FastAPI, Jinja, HTMX, Hono,
	Tailwind and Snowplow.