Hugoberry

Inspect Power BI files on macOS with PBIXRay

A practical guide to reading model metadata and exporting stored table data with Python.

Reviewed against the project documentation on 29 September 2026. Examples target PBIXRay 0.15.5.

What PBIXRay does

PBIXRay is an open-source Python library for reading the semantic model inside a Power BI .pbix file. It lets you inspect that model on a Mac without running Power BI Desktop or connecting to an Analysis Services server. Parsing happens locally; installing the package requires downloading it first. [1]

Hugoberry

meta:
  id: parquet
  title: Apache Parquet columnar storage format
  file-extension: parquet
  ks-version: 0.9
  endian: le
  license: CC0-1.0

doc: |
  Minimal Parquet container layout:

Hugoberry

# Project Policy

This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

# 1. Introduction

> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

## 1.1 Actors

Hugoberry

@{%
const moo = require('moo');

// Consolidated token patterns
const patterns = {
    // Whitespace and basic tokens
    ws: /[ \t]+/,
    newline: { match: /\n/, lineBreaks: true },
    colon: ':',
    equals: '=',

Hugoberry

@{%
const moo = require('moo');

// Consolidated token patterns
const patterns = {
    // Whitespace and basic tokens
    ws: /[ \t]+/,
    newline: { match: /\n/, lineBreaks: true },
    colon: ':',
    equals: '=',

Hugoberry

@{%
const moo = require('moo');

const lexer = moo.compile({
    ws: /[ \t]+/,
    newline: { match: /\n/, lineBreaks: true },
    colon: ':',
    equals: '=',
    lparen: '(',
    rparen: ')',

Hugoberry

import os
from typing import Any, Dict, Iterator, Union, List, Tuple
import sqlfluff

# Parses the given SQL file and returns the parsing result
def parse_sql_file(file_path: str) -> Dict[str, Any]:
    try:
        with open(file_path, 'r', encoding='utf-8-sig') as file:
            sql_content = file.read()
        parsed = sqlfluff.parse(sql_content, dialect="tsql")

Hugoberry

Google Sheets Script for Fetching Data from Supabase

This Google Sheets script fetches data from a Supabase database and writes the selected headers and data to the active sheet. The script first clears the sheet, writes the headers, then fetches the data from the Supabase API, and finally writes the data to the sheet. It is optimized and has anonymized variables for public use.

How to Use

1. Create a new Google Sheet or open an existing one.
2. Click on "Extensions" in the menu, then select "Apps Script." (If you don't see "Extensions," click on "Tools" and then "Script editor.")
3. In the Apps Script editor, replace the default Code.gs content with the content of supabase_to_google_sheets.gs.
4. Replace the SUPABASE_URL and SUPABASE_ANON_KEY variables with your actual Supabase URL and anon key.

Hugoberry

import duckdb
from duckdb import (
    ConstantExpression,
    ColumnExpression,
    FunctionExpression,
    StarExpression
)
conn = duckdb.connect(database='embed.ddb')

text="Wooden floor"

Hugoberry

evaluate 
var sample = 
{
("L", 5),
("N", 5),
("P", 7),
("E", 9),
("U", 11),
("Y", 11),
("V", 29),