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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.
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 231k Star，今日 +0，本周 +0，本月 +0。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 活跃；最近一次推送距今 1 天，未关闭 Issue 为 105，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：35k Fork 和 231k 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 13k。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
热度
231k 星标
复用
35k 复刻
关注
231k 关注者
维护
active
许可证
MIT
未解决 Issue
105
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.
Key Features
- 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-universal100
健康评分
活跃
提交活跃度
Jan 18, 2026
创建于
Jul 17, 2026
最近提交
+12k
今日增长
+13k
7天增长
+13k
30天增长
复刻
未解决
关注者
affaan-m
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EbookFoundation/free-programming-books
:books: Freely available programming books
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
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.
Capabilities
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.
Bottom Line
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.