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在 OpenClaw 中安装
/install zotero-vectorize
功能描述
Build and maintain a cross-platform local Zotero semantic index using metadata embeddings and PDF full-text chunk embeddings. Use when the user asks to vecto...
安全使用建议
This bundle appears to do what it says: it reads your local Zotero DB/storage, snapshots the DB, extracts PDF text, and builds local JSON vector stores. Before running: (1) review/confirm the output directory to avoid overwriting files; (2) run in a Python virtualenv and install the listed packages; (3) expect the embedding model to be downloaded (may be large and require network access) — you can pre-download models if offline; (4) close Zotero while snapshotting to avoid SQLite lock errors; (5) the safety rule to obtain user confirmation before applying incremental updates is procedural (the apply script does not itself prompt), so only run apply_incremental_updates after reviewing the check_incremental_updates report. If you need the skill to never download models automatically, inspect or modify get_embedding_model/encode_texts to load local models only.
功能分析
Type: OpenClaw Skill
Name: zotero-vectorize
Version: 0.1.0
The zotero-vectorize skill bundle is a legitimate tool designed to create a local semantic index of a Zotero library using embeddings. The scripts (e.g., build_metadata_vectors.py, build_fulltext_vectors.py) and the shared library (zotero_vectorize_lib.py) perform standard file operations, SQLite database snapshotting, and text extraction using well-known libraries like PyMuPDF and sentence-transformers. The instructions in SKILL.md emphasize safety, such as treating the Zotero database as read-only and requiring user confirmation before applying incremental updates, with no evidence of malicious intent, data exfiltration, or prompt injection.
能力评估
Purpose & Capability
Name/description match the included scripts and references: the code reads a Zotero SQLite DB and storage, extracts metadata and PDF text, creates embeddings, and writes local vector store files. Environment variables referenced (ZOTERO_DATA_DIR, ZOTERO_DB, ZOTERO_STORAGE, ZOTERO_VECTORS_DIR) are appropriate for locating Zotero data and output and are optional. No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md describes and limits runtime behavior to detecting Zotero paths, snapshotting the DB, extracting text, creating embeddings, verifying counts, and backing up/writing vector store files. The included scripts follow that flow and do not attempt to read unrelated system files or contact external endpoints (beyond embedding/model loading which is expected). Note: the workflow relies on creating DB snapshots and writing backups/output files; the 'ask for user confirmation before applying updates' is a process rule (scripts don't prompt interactively) — the agent/user must run check_incremental_updates first and then explicitly run apply_incremental_updates.
Install Mechanism
No install spec is included (instruction-only installation), and all code is provided. Dependencies are standard Python packages for embedding and PDF extraction (sentence-transformers, torch, PyMuPDF, numpy). There are no downloads from obscure URLs in the install phase. Runtime model downloads (HuggingFace/SentenceTransformers) may occur when loading embeddings — this is expected for the purpose, but is a network operation to be aware of.
Credentials
The skill requests no secrets or privileged environment variables. It optionally reads path-related env vars for convenience (ZOTERO_*), which are coherent with its function. There are no unrelated tokens, passwords, or cloud credentials requested.
Persistence & Privilege
always is false and the skill does not request permanent elevated platform privileges. It writes backups and store files only to the configured output directory and snapshots a user-provided Zotero DB path; it does not alter other skills or system-wide agent settings.
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 zotero-vectorize - 安装完成后，直接呼叫该 Skill 的名称或使用
/zotero-vectorize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
v0.1.0
- Initial release of zotero-vectorize.
- Build and maintain a local, cross-platform Zotero semantic index using embeddings for metadata and PDF full-text chunks.
- Strictly read-only input from Zotero; no modifications to user data.
- Comprehensive, step-by-step workflow for builds, incremental updates, backups, and verification.
- Detailed integration with reference files per platform and troubleshooting guidance.
- Automated reporting of counts, file sizes, and operational summaries after actions.
元数据
常见问题
Zotero Vectorize 是什么？
Build and maintain a cross-platform local Zotero semantic index using metadata embeddings and PDF full-text chunk embeddings. Use when the user asks to vecto... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 294 次。
如何安装 Zotero Vectorize？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install zotero-vectorize」即可一键安装，无需额外配置。
Zotero Vectorize 是免费的吗？
是的，Zotero Vectorize 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Zotero Vectorize 支持哪些平台？
Zotero Vectorize 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 Zotero Vectorize？
由 yckbz（@yckbz）开发并维护，当前版本 v0.1.0。
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