An analysis of how coding agents like Claude Code are designed, which breaks an agent into its basic parts and rebuilds it with minimal code: a rudimentary agent with skills, sub-agents and a to-do list in a few hundred lines of Python.
Offers comprehensive development guides for three distinct AI-powered demo projects with standardized workflows, strict code style guidelines, and containerization instructions.
All parts of Claude Code's system prompt, including builtin tool descriptions, sub agent prompts (Plan/Explore/Task), utility prompts (CLAUDE.md, compact, Bash cmd, security review, agent creation, etc.). Updated for each Claude Code version.
An approach to working with Skills that uses hooks to make Claude select and activate the right Skill for the current context. Documented, and adaptable to other projects and workflows.
A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use. Resources are in Korean but can produce high-quality English-language output.
35+ short Claude Code tips covering voice input, system prompt patching, container workflows for risky tasks, conversation cloning, multi-model orchestration with Gemini CLI and more, with demos, working scripts and a plugin.
An autonomous AI development framework that enables Claude Code to work iteratively on projects until completion. Features intelligent exit detection, rate limiting, circuit breaker patterns, and comprehensive safety guardrails to prevent infinite loops and API overuse. Built with Bash, integrated with tmux for live monitoring, and includes 75+ comprehensive tests.
Interactive plan review UI that intercepts ExitPlanMode via hooks, letting users visually annotate plans with comments, deletions, and replacements before approving or denying with detailed feedback.
A guide to Claude Code from beginner to power user, with templates for its features, guides on agentic workflows, quizzes and a cheatsheet. Check that it is current before relying on it.
Ralph Orchestrator implements the simple but effective "Ralph Wiggum" technique for autonomous task completion, continuously running an AI agent against a prompt file until the task is marked as complete or limits are reached. This implementation provides a robust, well-tested, and feature-complete orchestration system for AI-driven development. Also cited in the Anthropic Ralph plugin documentation.
A development environment for Claude Code with a spec-driven workflow, TDD enforcement, cross-session memory, semantic search, quality hooks and modular rules. Large, with wide coverage.