lguzzon

Self-Improving Claude Code: A Bootstrap Seed

The Hypothesis

A single prompt (~1400 tokens), placed in a project's .claude/CLAUDE.md, can bootstrap a Claude Code instance into a self-improving system — one that captures learnings, extracts patterns, evolves its own configuration, and gets meaningfully better at helping its user with each session.

No pre-built infrastructure required. No user-level config. No hooks, skills, templates, or elaborate folder hierarchies. Just a seed and the affordances Claude Code already provides.

Background

lguzzon

Using Claude Code with various AI providers

Create various settings file I have one file for each provider, all in ~/.claude

• KIMI K2.5: kimi_settings.json

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.moonshot.ai/anthropic",

lguzzon

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

bold() { printf "\033[1m%s\033[0m\n" "$*"; }
info() { printf "• %s\n" "$*"; }
warn() { printf "\033[33m! %s\033[0m\n" "$*"; }
err()  { printf "\033[31m✗ %s\033[0m\n" "$*" >&2; }
ok()   { printf "\033[32m✓ %s\033[0m\n" "$*"; }

require_macos() {

lguzzon

SPARC Framework Prompt Template

Introduction

You are an AI language model assisting in the development of a project using the SPARC framework, which consists of the following steps:

1. Specification
2. Pseudocode
3. Architecture
4. Refinement

lguzzon

# Create a new worktree and branch from within current git directory.
ga() {
  if [[ -z "$1" ]]; then
    echo "Usage: ga [branch name]"
    exit 1
  fi

  local branch="$1"
  local base="$(basename "$PWD")"
  local path="../${base}--${branch}"

lguzzon

Claude-SPARC Automated Development System For Claude Code

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Overview

The SPARC Automated Development System (claude-sparc.sh) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.

Features

lguzzon

{
  "mcpServers": {
    "count-r": {
      "command": "python",
      "args": [
        "C:\\Users\\mberm\\count-r-server\\server.py"
      ],
      "host": "127.0.0.1",
      "port": 8080,
      "timeout": 30000

lguzzon

from mcp.server.fastmcp import FastMCP
import time
import signal
import sys

# Handle SIGINT (Ctrl+C) gracefully
def signal_handler(sig, frame):
    print("Shutting down server gracefully...")
    sys.exit(0)

lguzzon

#!/usr/bin/env bash

set -euo pipefail

if [ "${1:-}" = "--help" ]; then
  cat << EOF
Usage: ijq [filename]

    A wrapper around jq that uses fzf to interactively build jq filters.

lguzzon

#!/usr/bin/env -S bash -c "docker run -p 8080:8080 -it --rm \$(docker build --progress plain -f \$0 . 2>&1 | tee /dev/stderr | grep -oP 'sha256:[0-9a-f]*')"

# syntax = docker/dockerfile:1.4.0

FROM node:20

WORKDIR /root

RUN npm install sqlite3