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memddc

作者 hq · GitHub ↗ · v1.0.2 · MIT-0
cross-platform ⚠ suspicious
73
总下载
1
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install memddc-ai-skill
功能描述
面向团队协作的项目文档管理与代码迭代工具,支持自动文档生成、DDD模型管理、记忆压缩和智能变更同步。
安全使用建议
This skill appears to do what it claims (scan the repo, build an index, generate docs, and update code/docs based on a mem-snapshot), but it performs broad repository scans and reads IDE indexes, config files and VCS logs — which can include secrets or sensitive info. Before installing/use: 1) Review and vet the README/SKILL.md locally; run it on a non-production or sample repository first. 2) Add strict excludes to .memddc/config.json (or to your repository) to prevent scanning of .env, credential files, keystores, CI secrets, and other sensitive paths. 3) Ensure .memddc/ is included in .gitignore so snapshots and logs aren't accidentally committed. 4) Prefer manual invocation (avoid scheduling/autonomous runs) until you verify what artifacts are produced. 5) Because the publisher/source is unknown (no homepage), inspect the example outputs under example/.memddc and verify there are no hidden remote endpoints or implicit uploads. If you need help auditing the exact set of files the skill will read or the contents of a mem-snapshot.json produced in your repo, run initialization in an isolated clone and review the snapshot before allowing any external transmission.
功能分析
Type: OpenClaw Skill Name: memddc-ai-skill Version: 1.0.2 MemDDC is a project documentation and code iteration tool designed to optimize AI agent performance by maintaining a structured 'memory snapshot' (mem-snapshot.json). It automates the generation of DDD models, architecture diagrams, and API documentation by scanning the codebase and analyzing VCS (Git/SVN) logs. While the skill requires broad file system access and executes Git commands (e.g., `git log`, `git diff`) to track changes and build context, these actions are strictly aligned with its stated purpose of project governance and token-usage reduction. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found in the SKILL.md or README.md files.
能力评估
Purpose & Capability
Name/description (project doc management, DDD model management, memory compression, sync) align with runtime instructions: repo scanning, git log/diff, mem-snapshot.json indexing, targeted doc updates and code-change workflows. The behavior requested (reading project files, building an index, updating markdown and snapshots) is coherent with the stated purpose.
Instruction Scope
SKILL.md instructs full recursive scanning of the project, collecting IDE index files, running git commands (git log, git diff), performing AST/code scans, reading/writing .memddc/* (mem-snapshot.json, logs, compressed archives) and 'sending' logs/file-structure/user-docs to the AI for analysis. There are no explicit exclusions for sensitive paths (e.g., .env, credential files) and the skill's 'active request' capability includes asking for DB table samples and other potentially sensitive artifacts. While the SKILL.md limits exploratory reads by recommending index lookups first, the scanning steps still permit broad access to repository contents — a potential privacy/secret exposure risk.
Install Mechanism
Instruction-only skill with no install spec and no code files to write by the installer. This lowers supply-chain risk because nothing is downloaded or installed by an automated installer.
Credentials
The skill declares no required environment variables or external credentials (good), but it instructs the agent to read many local files (IDE indexes, VCS logs, config files) and to request sample DB schemas or code snippets from users. That broad file access can surface secrets (API keys, DB credentials) even though no environment variables are requested. The lack of explicit exclusions or guidance about skipping secrets/configuration files is a proportionality concern.
Persistence & Privilege
Does not request always:true and is user-invocable. It will create and write a .memddc/ directory, mem-snapshot.json, logs and archives inside the project — normal for this tool. Because the skill can be invoked autonomously by agents (default), combining autonomous runs with broad file scanning increases the blast radius; this is a caution, not an immediate disqualifier.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install memddc-ai-skill
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /memddc-ai-skill 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.2
MemDDC 1.0.2 – 主要功能与结构全面升级 - 新增 VCS 日志(Git/SVN)AI分析,自动归档提交历史,支持团队协作模式洞察 - 引入项目三级索引快照结构(metadata/index/context),保障变更与信息精准定位 - 支持文档、业务、架构、API等多维度文档自动生成与分析,纳入用户自定义文档 - 实现 entity→mapper→service→controller→view 关联映射和领域驱动(DDD)建模同步 - 文件/代码/结构/配置变更可精准识别影响范围,实现同步闭环与自动记忆压缩 - 优化目录结构,提升团队共享与大规模工程持续协作效率
元数据
Slug memddc-ai-skill
版本 1.0.2
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

memddc 是什么?

面向团队协作的项目文档管理与代码迭代工具,支持自动文档生成、DDD模型管理、记忆压缩和智能变更同步。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 73 次。

如何安装 memddc?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install memddc-ai-skill」即可一键安装,无需额外配置。

memddc 是免费的吗?

是的,memddc 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

memddc 支持哪些平台?

memddc 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 memddc?

由 hq(@qihao123)开发并维护,当前版本 v1.0.2。

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