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better agentic 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。
microsoft/AI-Engineering-Coach 被追踪为 TypeScript 项目，主要属于 AI Agent 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 3.4k Star，今日 +61，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 3 天，未关闭 Issue 为 49，约占总 Star 的 1.45%。这只是采用信号，不替代工程尽调。
采用检查：491 Fork 和 3.4k 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「TypeScript 技术栈团队评估生态原生工具」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 2 个明确来源引用，近期增长信号为 61。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
热度
3.4k 星标
复用
491 复刻
关注
3.4k 关注者
维护
active
许可证
MIT
未解决 Issue
49
AI Engineer Coach is a VS Code extension that analyzes local AI coding assistant session logs to provide actionable insights, including practice scores, anti-pattern detection, code output metrics, and skill discovery, all without sending data off the machine.
Key Features
- Tracks practice scores, weekly trends, and daily activity charts - Detects 45 anti-patterns across prompt quality, session hygiene, code review, tool mastery, and context management - Measures AI-generated code volume by language, workspace, model, and harness
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
git clone https://github.com/microsoft/ai-engineering-coach.git && cd ai-engineering-coach && npm ci && npm run package && code --install-extension ai-engineer-coach-*.vsix83
健康评分
活跃
提交活跃度
May 6, 2026
创建于
Jul 18, 2026
最近提交
+0
今日增长
+0
7天增长
+0
30天增长
复刻
未解决
关注者
microsoft
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✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
It solves the lack of visibility and feedback loops in AI-assisted coding workflows, where developers often have no structured way to evaluate prompt quality, session hygiene, or code output volume. Unlike generic analytics tools, it provides 45 anti-pattern rules, context health scoring, and skill discovery tailored specifically for agentic engineering.
Capabilities
Developers can build a data-driven practice of AI coding by tracking weekly trends, detecting anti-patterns in prompts and sessions, measuring AI-generated code volume by language or model, discovering reusable skills from repeated prompts, and scoring context health. Real-world use cases include improving team-wide AI coding efficiency, onboarding new developers to best practices, and auditing AI usage for quality assurance.
Bottom Line
This tool is ideal for developers and teams who want to systematically improve their AI coding assistant usage through local, privacy-preserving analytics. It is less suited for those who do not use AI coding assistants or prefer cloud-based analytics. The key trade-off is the need for manual build and installation versus the benefit of fully offline, actionable insights.