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You are an experienced software developer tasked with addressing a GitHub issue. Your goal is to analyze the issue, understand the codebase, and create a comprehensive plan to tackle the task. Follow these steps carefully:

  1. First, review the GitHub issue using the gh issue view command.

<github_issue> #$ARGUMENTS </github_issue>

  1. Next, examine the relevant parts of the codebase.

Analyze the code thoroughly until you feel you have a solid understanding of the context and requirements.

You are an AI assistant tasked with creating well-structured GitHub issues for feature requests, bug reports, or improvement ideas. Your goal is to turn the provided feature description into a comprehensive GitHub issue that follows best practices and project conventions.

First, you will be given a feature description and a repository URL. Here they are:

<feature_description> #$ARGUMENTS </feature_description>

Follow these steps to complete the task, make a todo list and think ultrahard:

  1. Research the repository:
// ----------------------------------------------------------------------------
// 🚨 STEP 1. 🚨
// Change 'Test' to the name of your table.
let table = base.getTable('Test');
// ----------------------------------------------------------------------------
// ----------------------------------------------------------------------------
// 🚨 STEP 2: 🚨
// Change the following url to your Zapier webhook.
// To get a Zapier webhook, click the below link, then choose "Catch Hook" as the trigger event
@lukecav
lukecav / DNS Prefetch domains
Last active July 14, 2025 05:04
WP Rocket - Advanced Options Prefetch DNS requests examples
//maps.googleapis.com
//maps.gstatic.com
//fonts.googleapis.com
//fonts.gstatic.com
//ajax.googleapis.com
//apis.google.com
//google-analytics.com
//www.google-analytics.com
//ssl.google-analytics.com
//youtube.com
@thegitfather
thegitfather / vanilla-js-cheatsheet.md
Last active July 17, 2025 02:58
Vanilla JavaScript Quick Reference / Cheatsheet
library(Matrix)
library(igraph)
library(plyr)
library(Rfacebook)
library(rjson)
library(Hmisc)
###########################################################
# I. stazeni postu a komentaru ze stranky a jejch ulozeni #
###########################################################
@akora
akora / remove-diacritics-google-sheet
Created February 28, 2015 11:18
Removes all diacritics from strings (e.g. names) in a Google spreadsheet
=SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(C2,"ö","o"),"ü","u"),"ó","o"),"ő","o"),"ú","u"),"é","e"),"á","a"),"ű","u"),"í","i"),"Ö","O"),"Ü","U"),"Ó","O"),"Ő","O"),"Ú","U"),"É","E"),"Á","A"),"Ű","U"),"Í","I")
@josefslerka
josefslerka / gist:4195326
Created December 3, 2012 14:23
Digital Humanities 8 - Text mining
#
#
# https://sites.google.com/site/genbiovis/code/network
#
# natazeni knihovny
library(tm)
# nacteni korpusu
@josefslerka
josefslerka / ncd.R
Created December 1, 2012 19:39
Normalized Compression Distance
# Normalized Compression Distance
# http://en.wikipedia.org/wiki/Normalized_Compression_Distance
# Quick and dirty implementation
ncd <- function(file1, file2) {
both <- ""
file1 <- toString(file1)
file2 <- toString(file2)
comp1 <- memCompress(file1, type=c(c("bzip2")))
@josefslerka
josefslerka / gist:2344144
Created April 9, 2012 15:09
Wordlcloud pro Vodafone
library(tm)
require(wordcloud)
require(RColorBrewer)
# zde je treba vlozit korpus
# mydata.vectors <- character(0)
# mydata.vectors <- c("Dobre dopoledne Mateji,pokud jste pri registraci zvolil Moravskoslezsky kraj, zobrazila se vam v aplikaci informace, ze soutez probehne 30.1. Vcera 31.1. sef gangu navstivil Olomoucky a Zlinsky kraj. Zvoleny kraj naleznete v aplikaci v zalozce "Souboj kraju".Kazdopadne dnes hrajeme o 75 telefonu Samsung GALAXY mini. Online kolo zacina jiz ve 13:00.Drzim palce, Sandra, O2 Guru", mydata.vectors)
corpus <- Corpus(VectorSource(mydata.vectors))
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, removePunctuation)