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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -224,6 +224,7 @@ RL-trained models that run up to 8 parallel searches per turn to retrieve code c
- **[pro-workflow](https://github.com/rohitg00/pro-workflow)**: Self-correcting memory system that lets Claude Code learn from its mistakes, covering context engineering, parallel worktrees, agent teams, and 17 skills
- **[kit](https://github.com/cased/kit)**: Context engineering toolkit for AI dev tools, providing codebase mapping, symbol extraction, and code search
- **[RepoPrompt CE](https://github.com/repoprompt/repoprompt-ce)**: Community edition of RepoPrompt, a native macOS context engineering app for AI coding agents, with an MCP CLI
- **[DevProjex](https://github.com/Avazbek22/DevProjex)**: Builds clean, AI-ready project context with folder trees, file contents, token counting, Smart Ignore, preview, and multi-format export through a fast GUI and CLI.
- **[context-engineering (outcomeops)](https://github.com/outcomeops/context-engineering)**: Working reference implementation of context engineering with five components (corpus, retrieval, injection, output, enforcement), running end-to-end on Amazon Bedrock
- **[Project Context Records (PCR)](https://github.com/hyf0/project-context-records)**: A context-engineering methodology that keeps a durable, repo-versioned archive of a project's meta-context (the why, architecture, and maintainer decisions) so AI collaborators inherit its judgment instead of re-deriving it
- **[interview-prep-template](https://github.com/AbhiK189/interview-prep-template)**: A three-layer context-engineering template (immutable sources → agent-maintained wiki → operating-manual file) where the agent synthesizes raw material into reusable answers, frameworks, and scored debriefs that compound over time
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1 change: 1 addition & 0 deletions README_CN.md
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Expand Up @@ -218,6 +218,7 @@ Context工程是对大语言模型(LLM)信息负载的系统性优化。它包
- **[pro-workflow](https://github.com/rohitg00/pro-workflow)**:让 Claude Code 从纠错中学习的自我修正记忆系统,涵盖上下文工程、并行 worktree、agent 团队与 17 个 skills
- **[kit](https://github.com/cased/kit)**:面向 AI 开发工具的上下文工程工具包,提供代码库映射、符号抽取与代码搜索
- **[RepoPrompt CE](https://github.com/repoprompt/repoprompt-ce)**:RepoPrompt 社区版,面向 AI 编码 agent 的 macOS 原生上下文工程应用,附带 MCP CLI
- **[DevProjex](https://github.com/Avazbek22/DevProjex)**:通过快速的 GUI 和 CLI,使用目录树、文件内容、Token 统计、Smart Ignore、预览和多格式导出,构建整洁且适用于 AI 的项目上下文。
- **[context-engineering(outcomeops)](https://github.com/outcomeops/context-engineering)**:上下文工程的可运行参考实现,含语料、检索、注入、输出、强制执行五大组件,在 Amazon Bedrock 上端到端运行
- **[Project Context Records (PCR)](https://github.com/hyf0/project-context-records)**:一套上下文工程方法论,在仓库内持久化、版本化地存档项目的「元上下文」(缘由、架构、维护者决策),让 AI 协作者继承项目判断力,而非反复重新推导
- **[interview-prep-template](https://github.com/AbhiK189/interview-prep-template)**:三层上下文工程模板(不可变原始素材 → agent 维护的 wiki → 运行手册文件),由 agent 把原始素材合成为可复用的答案、框架与带评分的复盘,并随每次面试持续累积变强
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