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在 OpenClaw 中安装
/install memory-semantic-search
功能描述
Semantic search over workspace markdown files using embedding API + SQLite vector store. Use when: (1) searching workspace notes/memory by meaning rather tha...
安全使用建议
This skill appears to implement a legitimate local Markdown semantic-search tool, but take these precautions before installing/using it:
- Expect to provide an embeddings API key (EMBEDDING_API_KEY); the registry metadata omitted this — verify environment requirements before trusting the package.
- By default it will send the full text of your Markdown chunks to the configured embedding endpoint (default: https://api.openai.com/v1). Only use a provider you trust, or configure a self-hosted/enterprise-compatible embedding endpoint if your notes are sensitive.
- Avoid indexing secrets or credentials. Use a workspace path that excludes sensitive files, or add exclusions before running index.py.
- Consider setting --db to a controlled path (not a global skill directory) and protect that SQLite file appropriately.
- Review/verify EMBEDDING_API_BASE if you need embeddings to stay in your environment (e.g., Ollama, internal proxy). If you need privacy guarantees, confirm the embedding provider’s retention policy.
- The main technical inconsistency is the missing declared env vars in the registry; if this skill will run in an automated agent environment, confirm the platform will surface the required API key prompt before the skill runs.
If you want me to, I can: (1) point out the exact lines that transmit data to the network, (2) suggest a small patch to redact or exclude sensitive files before indexing, or (3) show how to change the default DB path and embedding base in the code.
功能分析
Type: OpenClaw Skill
Name: memory-semantic-search
Version: 1.0.0
The skill bundle provides a legitimate semantic search utility for markdown files using Python's standard library. It indexes workspace content into a local SQLite database and uses a user-configured OpenAI-compatible API for generating embeddings (scripts/index.py and scripts/search.py). The behavior is transparent, well-documented, and strictly follows the stated purpose without any signs of malicious intent, obfuscation, or prompt injection.
能力标签
能力评估
Purpose & Capability
The name/description (semantic search over workspace Markdown) matches the included code and instructions: index.py scans .md files, chunks them, calls an OpenAI-compatible embeddings endpoint, and stores vectors in SQLite for search. However, the registry metadata lists no required environment variables while the SKILL.md and scripts clearly expect EMBEDDING_API_KEY (and optionally EMBEDDING_API_BASE / EMBEDDING_MODEL). That omission in the metadata is an inconsistency.
Instruction Scope
SKILL.md and the scripts limit actions to scanning .md files in a provided workspace, chunking, calling an embeddings API, storing embeddings in a local SQLite DB, and performing local cosine-similarity search. There are no instructions to read unrelated system files or other credentials. NOTE: the runtime does transmit Markdown content to the configured embedding API endpoint, which is expected for this purpose but important to be aware of.
Install Mechanism
This is an instruction-only skill with shipped Python scripts and no install spec; nothing is downloaded at install time. That minimizes install-time risk. The code uses only Python stdlib and will be run locally.
Credentials
The scripts require EMBEDDING_API_KEY (and optionally EMBEDDING_API_BASE and EMBEDDING_MODEL). The registry metadata claims no required env vars — that is inconsistent. Also, by design the skill sends full Markdown chunks to the embedding API (default EMBEDDING_API_BASE is https://api.openai.com/v1). Sending sensitive notes to an external provider can expose data (some embedding providers log/retain inputs). The requested credential (API key) is proportional to the feature, but the lack of declared required env vars in the registry and the default external endpoint raise privacy/visibility concerns.
Persistence & Privilege
The skill does not request elevated privileges and always=false. It writes a SQLite DB file by default to the skill parent directory (memory_search.sqlite) unless a custom --db is provided; this is normal but the user should be aware of where indexed content is stored. It does not modify other skills or system-wide configs.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install memory-semantic-search - 安装完成后，直接呼叫该 Skill 的名称或使用
/memory-semantic-search触发 - 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
v1.0.0
- Initial release of memory-semantic-search: semantic search for workspace markdown files
- Supports indexing and searching `.md` files by meaning using OpenAI-compatible embedding APIs and SQLite vector storage
- Environment variables/CLI for API key, base URL, and model selection
- Indexing tool: incremental, tracks changes and cleans up deleted files
- Search tool: configurable top-k results, score threshold, and JSON output
- Designed for semantic recall of notes, related content, and decisions in markdown—excludes non-markdown, web, or code search
元数据
常见问题
Memory Semantic Search 是什么？
Semantic search over workspace markdown files using embedding API + SQLite vector store. Use when: (1) searching workspace notes/memory by meaning rather tha... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 84 次。
如何安装 Memory Semantic Search？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install memory-semantic-search」即可一键安装，无需额外配置。
Memory Semantic Search 是免费的吗？
是的，Memory Semantic Search 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Memory Semantic Search 支持哪些平台？
Memory Semantic Search 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 Memory Semantic Search？
由 toller892（@toller892）开发并维护，当前版本 v1.0.0。
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