nibzard

name	description	tools	model
git-master	Git operations specialist for autonomous version control. Use PROACTIVELY after completing any coding task to commit changes. MUST BE USED for all git operations, commits, and repository management.	Bash, Read, Grep, Glob	inherit

You are an autonomous git specialist responsible for version control operations and maintaining clean commit history.

CORE RESPONSIBILITIES:

nibzard

name	description	tools	model
task-master	Autonomous task management expert for handling todo files and task prioritization. Use PROACTIVELY for all task selection, status updates, and todo file management. MUST BE USED when working with any todo or task files.	Read, Edit, MultiEdit, Grep, Glob, TodoWrite	inherit

You are an autonomous task management specialist focused on efficiently processing todo lists and managing task workflows.

CRITICAL RESPONSIBILITIES:

nibzard

#!/bin/bash

################################################################################
# AUTONOMOUS AI TASK PROCESSOR
# https://gist.github.com/nibzard/a97ef0a1919328bcbc6a224a5d2cfc78
################################################################################
#
# PURPOSE:
#   Runs Claude Code in a fully autonomous loop to process tasks from a todo
#   file without human intervention. The AI agent will continuously select,

nibzard

                    ┌─────────────────────┐
                    │   CONTENT CREATION  │
                    │  (Blog, Talks, AI)  │
                    └──────────┬──────────┘
                               ▼
              ┌─────────────────────────────────────┐
              │         AUDIENCE GROWTH             │
              └─────────────────┬───────────────────┘
                                ▼
    ┌─────────────┐    ┌─────────────────────┐    ┌─────────────┐

nibzard

#!/usr/bin/env bash
set -euo pipefail

#########################################################################
# scribe
#
# 1) Converts any audio/video file (mp4, m4a, wav, etc.) → 16 kHz mono WAV
# 2) Uses whisper.cpp (via whisper-cli) to produce a text transcript
# 3) If a prompt is found (either a user-passed prompt file OR a “prompt.md”
#    in the same folder), pipes that transcript into llm to generate a Markdown

nibzard

<EPISODE 1> Overall Theme: The Dawn of Agentic AI in Software Development

The first episode of “Raising an Agent” introduces listeners to Thorsten Ball and Quinn Slack’s pioneering journey as they build an AI-powered coding assistant from the ground up. In this inaugural session, they share their excitement over the agent’s surprisingly autonomous problem-solving, reveal the foundational “inversion of control” mindset that lets the model orchestrate tasks with minimal human prompting, and highlight the emergent behaviors—like self-debugging and tool-use—that transformed their rough prototype into a capable collaborator. Throughout, they offer a candid look at both the triumphs and the early challenges of wiring together language models, developer tooling, and rich feedback loops, setting the stage for a deeper exploration of what it means to treat an LLM not just as a generator of text but as an autonomous software engineer.

1. Podcast & Project Overview:

• Podcast Name: "Raising an Agent."

nibzard

#!/usr/bin/env bash
source <(curl -s https://raw.githubusercontent.com/community-scripts/ProxmoxVE/main/misc/build.func)

# App Default Values
APP="Daytona"
var_tags="docker;development;daytona"
var_cpu="2"
var_ram="4096"
var_disk="20"
var_os="ubuntu"

nibzard

#!/bin/bash

# Validate that the argument is an integer
if [[ ! $1 =~ ^[0-9]+$ ]]; then
  echo "Please provide a valid integer as the argument."
  exit 1
fi

# Make API call, parse and summarize the discussion
curl -s "https://hn.algolia.com/api/v1/items/$1" | \

nibzard

You are an expert Python code refactoring engineer with extensive knowledge of software design principles, clean code practices, and Python-specific idioms. Your task is to analyze Python code snippets or entire modules, and provide detailed refactoring suggestions to improve code quality, readability, efficiency, and maintainability.

When presented with Python code, you should:

1. Identify and explain code smells, anti-patterns, or areas that violate PEP 8 style guidelines.

2. Suggest refactoring techniques such as extracting methods, introducing design patterns, or applying SOLID principles where appropriate.

3. Optimize code for performance and memory usage without sacrificing readability.

nibzard

!pip install reportlab
!pip install pypdf2

import random
import math
from numba import njit, cuda
import numpy as np
from reportlab.lib.pagesizes import A4
from reportlab.pdfgen import canvas
from reportlab.lib.units import cm