隐私与广告选择
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The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
这个仓库详情页对访问者有帮助，但在补足原创证据和对比上下文前，Git-Stars 会暂时避免让它进入搜索索引。
90
评测分
解决的问题
ECC solves the fragmentation of agent configurations across different AI coding harnesses by providing a single, reusable system that works with Claude Code, Codex, Cursor, and others. It also addresses the lack of built-in performance optimization, security scanning, and continuous learning in agent workflows, enabling more reliable and efficient agentic development.
部署现实
当前可用的安装信号是：npm install ecc-universal。这只能作为起点，正式使用前建议让 Codex 或 Claude 检查 README、环境变量、运行时版本和部署目标。
商业可用性
当前记录的许可证是 MIT。这是重要的商业使用信号，但仍应核验许可证全文、依赖许可证、模型/API 条款和商标限制。
能力上限
Developers can build production-ready AI agent workflows that include custom skills, instinct-driven behaviors, memory optimization, and automated security scanning. Real-world use cases include automated code review, multi-harness agent orchestration, and research-first development pipelines. The ceiling is high: with 211K+ stars and 230+ contributors, it supports complex, cross-platform agent systems for enterprise-grade software engineering.
Git-Stars 是独立产品，不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成，依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。
affaan-m/ECC 被追踪为 JavaScript 项目，主要属于 AI Agent, MCP Server, LLM Tool 方向。这个评估结合公开 GitHub 元数据、分类信号、短来源摘要和 Git-Stars 编辑规则，而不是复制项目文档。
增长检查：该仓库目前有 240k Star，今日 +143，本周 +1.5k，本月 +9.6k。这些窗口用于区分持续采用信号和短期曝光峰值。
维护检查：当前活跃度为 一般；最近一次推送距今 59 天，未关闭 Issue 为 122，约占总 Star 的 0.05%。这只是采用信号，不替代工程尽调。
采用检查：36k Fork 和 240k Watcher 反映项目被复用和关注的程度。许可证信号：MIT。商业或内部使用前请核验许可证兼容性。
适用判断：当你需要「AI 原型、LLM 工作流和 Agent 类应用」时，这个项目更值得评估；如果「需要法律审查、安全审计或生产 SLA 保证」，则需要谨慎。
来源检查：Git-Stars 当前为这份报告保留了 1 个明确来源引用，近期增长信号为 9.6k。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
原创分析
充分已经具备问题、能力和结论层面的原创判断。
来源
不足需要更清晰的来源引用。
许可证
充分已记录 MIT，可作为评估依据。
维护
充分仓库元数据中可以看到近期活跃信号。
同类对比
充分已有足够相近项目用于对比。
热度
240k 星标
复用
36k 复刻
关注
240k 关注者
维护
moderate
许可证
MIT
未解决 Issue
122
ECC is a harness-native operator system for agentic work, providing a complete system of skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. It works across multiple AI agent harnesses including Codex, Claude Code, Cursor, OpenCode, Gemini, Zed, and GitHub Copilot.
核心特性
- Cross-harness agent workflows supporting 7+ AI agent harnesses - Built-in security scanning (AgentShield) and continuous learning - Production-ready skills, hooks, rules, MCP configurations, and legacy command shims
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
MCP Server
Model Context Protocol servers, clients, and integrations
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
npm install ecc-universal80
健康评分
一般
提交活跃度
Jan 18, 2026
创建于
Aug 15, 2026
最近提交
每日同步积累数据后，Star 历史将在此显示。
复刻
未解决
关注者
affaan-m
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✓
许可证
✓
Fork 情况
~ 一般
维护情况
解决的问题
ECC solves the fragmentation of agent configurations across different AI coding harnesses by providing a single, reusable system that works with Claude Code, Codex, Cursor, and others. It also addresses the lack of built-in performance optimization, security scanning, and continuous learning in agent workflows, enabling more reliable and efficient agentic development.
能力范围
Developers can build production-ready AI agent workflows that include custom skills, instinct-driven behaviors, memory optimization, and automated security scanning. Real-world use cases include automated code review, multi-harness agent orchestration, and research-first development pipelines. The ceiling is high: with 211K+ stars and 230+ contributors, it supports complex, cross-platform agent systems for enterprise-grade software engineering.
结论
ECC is ideal for developers and teams using multiple AI coding agents who want a unified, optimized, and secure workflow. It may be overkill for single-harness users or those seeking a simple configuration tool. The key trade-off is its comprehensive feature set versus the initial complexity of setup and learning.