# DevRune: A Package Manager for AI Agent Instructions

> A CLI tool developed in Go that provides unified skill package management for multiple AI coding agents like Claude Code, Codex, and Copilot, automatically configuring cross-platform working environments via a single manifest file.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-18T18:14:21.000Z
- 最近活动: 2026-04-18T18:19:43.971Z
- 热度: 152.9
- 关键词: AI智能体, Claude Code, Codex, Copilot, 包管理器, 开发工具, Go语言, MCP, 技能管理
- 页面链接: https://www.zingnex.cn/en/forum/thread/devrune-ai
- Canonical: https://www.zingnex.cn/forum/thread/devrune-ai
- Markdown 来源: floors_fallback

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## [Introduction] DevRune: A Unified Package Manager for AI Agent Instructions

DevRune is a CLI tool developed in Go, designed to provide unified skill package management for multiple AI coding agents such as Claude Code, Codex, and Copilot. It automatically configures cross-platform working environments through a single manifest file, solving the problem of fragmented configurations across different agents. Its core philosophy is "one configuration, full platform adaptation", drawing on the npm package management approach to significantly improve the efficiency of multi-agent development.

## Problem Background: The Challenge of Fragmented AI Agent Configurations

With the popularity of AI coding agents, developers face configuration management challenges. Different platforms (Claude Code, Codex, Copilot, etc.) use different configuration formats and directory structures: Claude Code uses the .claude/ directory and markdown skill files, Codex uses the .codex/ directory and TOML configurations, and Copilot uses the .github/ directory and .agent.md files. Manually maintaining multi-agent configurations is cumbersome, which led to the birth of DevRune.

## Core Approach: Unified Configuration and Multi-Agent Support

The core of DevRune is "one configuration, full platform adaptation", drawing on the npm package management concept. Users write a devrune.yaml configuration to declare skills and tools, and the tool automatically handles parsing (fetching packages, generating lock files), installation (rendering into native formats for each platform), and state tracking. Currently, it supports 5 mainstream agents, with automatic deduplication of shared skill files.

## Technical Architecture: Three-Stage Pipeline and Intelligent Recommendation

DevRune uses a three-stage pipeline: manifest (devrune.yaml) → resolve (fetch packages and generate lock) → install (render into workspace files). It integrates an AI recommendation system that analyzes the project's tech stack to recommend skill packages, and integrates with skills.sh to provide security-audited skill packages.

## Usage Examples and Developer Experience

A typical devrune.yaml configuration includes sections like packages, mcps, agents, and workflows. The standardized directory structure facilitates sharing and reuse. For developer experience, there is an interactive TUI wizard (devrune init), CI/CD friendliness (--non-interactive), and offline support (--offline).

## Technology Selection and Current Limitations

DevRune is developed in Go, relying on Cobra (CLI framework), Bubble Tea (TUI), go-enry (language detection), etc., to ensure high performance and cross-platform compatibility. Current limitations: no native Windows support (WSL is recommended), and AI recommendations require installing Claude/OpenCode and logging in.

## Future Outlook and Conclusion

Future plans include supporting more agents, skill package version management, community registries, and visual editors. DevRune aims to establish a unified management standard for AI agent instructions, just as npm unified JS package management, to improve the efficiency of multi-agent developers, marking the maturity of the AI toolchain.