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.
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.
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",
Opinionated new Mac installation script — terminal settings, apps, git config and more (VS Code, iTerm2, Atuin, uv)
brew.txt and brew_cask.txt to your likingbash install.sh
| 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. |
| {% 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) %} |
| # 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 |
| 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' |
| 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` |