jimbrig

Published: 2025-10-18

Author: Karo Zieminski

URL: https://karozieminski.substack.com/p/claude-skills-anthropic-viral-toolkit-agentic-workflows-community-guide

About

A growing library of repos, videos and skills for Claude Skills (100+)

jimbrig

UPDATED 22.11.2022

It's been two years since the last update, so here's the updated working script as per the comments below.

Thanks to BryanHaley for this.

setInterval(function () {
    video = document.getElementsByTagName('ytd-playlist-video-renderer')[0];

 video.querySelector('#primary button[aria-label="Action menu"]').click();

jimbrig

# an example server: acquaint's MCP server for R sessions
r_process <- callr::r_bg(
  function() acquaint::mcp_server(),
  stdout = "|",
  stdin = "|"
)

Sys.sleep(1)

log_cat_client <- function(x, append = TRUE) {

jimbrig

Comprehensive Guide to Review and Optimize Windows Registry on Windows 11 with AI Support

This guide provides a comprehensive PowerShell script that allows you to backup, restore, analyze, and optimize your Windows Registry. Additionally, it integrates the OpenAI GPT-4 API to provide AI-powered analysis of your registry settings.

Table of Contents

1. Prepare Your System
2. Set Up OpenAI API Access
3. Enable Script Execution
4. The Enhanced Registry Optimization Script

jimbrig

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

jimbrig

options(shiny.maxRequestSize = 5 * 1024^2)  # Limit uploads to 5 MB

library(shiny)
library(magick)
library(base64enc)
library(bslib)

# Helper function to sanitize file names
safeFileName <- function(filename) {
  gsub("[^a-zA-Z0-9_.-]", "_", filename)

jimbrig

# This is a sample R Shiny app chatbot that runs local models with an Ollama server.
# You also need Ollama installed and a ollama server running, plus at least one local model pulled.
# I hard-coded a few local models,
# If you use this, you'll want to hard code yours (including descriptions, or take those out)

# Coded with lots of LLM help + Posit's Shiny Assistant.

library(shiny)
library(shinychat)
library(bslib)

jimbrig

library(elmer)
library(tidyverse)
library(DiagrammeR)
library(glue)

code <- "starwars %>%
  group_by(species) %>%
  summarise(
    n = n(),
    mass = mean(mass, na.rm = TRUE)

jimbrig

library(shiny)
library(reactable)
library(dplyr)

ui <- fluidPage(
  actionButton(inputId = "edit",
              label = "Edit"),
  reactableOutput("table")
)

jimbrig

library(shiny)

reactiveTrigger <- function() {
  counter <- reactiveVal( 0)
  list(
    depend = function() {
      counter()
      invisible()
    },
    trigger = function() {