This repository is a production-grade meta-framework for building and managing AI agent plugins across multiple coding assistants (Claude Code, Codex, Cursor, OpenCode, Gemini, Copilot). It establishes a single source of truth in Markdown (`plugins/`) that is compiled into harness-specific artifacts via Python adapters. Key features include a security layer using Cedar policies for tool-call authorization and signed audit receipts, a project management system (`conductor`) for track-based development, and a quality evaluation framework (`plugin-eval`) using static analysis and LLM-based scoring. It solves the fragmentation problem of AI agent ecosystems by enforcing portability and governance.
Ponytail is a skill ecosystem for AI coding agents that enforces a 'lazy senior developer' mindset through a priority ladder: YAGNI, reuse existing code, prefer stdlib/native features, avoid unrequested abstractions, and write minimal working code. It delivers this via platform-specific adapters (hooks, command files, plugin manifests) for agents like Claude Code, Codex, GitHub Copilot, OpenCode, Gemini CLI, and others. The core skill definition lives in skills/ponytail/SKILL.md, with companion skills for review, audit, debt tracking, gain scoring, and help. The system includes automated validation scripts to maintain consistency across platform-specific rule copies and version manifests, benchmark suites to measure code/token/cost reductions, and test coverage for hook behavior and plugin integrity.
A Claude Code and Cursor plugin that delivers behavioral guidelines for AI coding assistants, derived from Andrej Karpathy's observations about LLM coding pitfalls. The core artifact is a single CLAUDE.md file (and its Cursor rules equivalent) encoding four principles: Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution. The repository packages this file as a Claude Code plugin with marketplace metadata, a Cursor project rule with alwaysApply, and a reusable skill definition. The EXAMPLES.md file provides detailed before/after code examples demonstrating each principle. The entire functional surface is documentation-as-configuration — there is no application logic, data processing, or runtime code. The plugin packaging is clean with proper marketplace.json and plugin.json manifests. The Cursor rule is correctly structured with frontmatter. The examples are concrete and well-annotated, showing realistic LLM mistakes and corrections. The Chinese README translation is a nice touch for accessibility. This is essentially a configuration and documentation project that delivers genuine value through carefully crafted AI assistant instructions.