by 安全扫描
OpenClaw
安全
high confidenceThe skill implements the described RAG retriever (local Xenova + optional OpenAI embeddings, BM25, RRF fusion, Cross‑Encoder reranking and citation management); its code, docs and CLI are internally consistent and there are no obvious attempts to exfiltrate unrelated secrets or contact hidden endpoints.
评估建议
This skill appears to implement the advertised RAG functionality and is internally consistent. Before installing, consider: (1) OPENAI_API_KEY is used optionally but not listed as a required env var — only provide it if you want cloud embeddings. (2) The skill stores data and config locally (.rag3-config.json and a LanceDB data folder) — run it in a directory you control and inspect created files. (3) Xenova/transformers or other runtime libs may download model weights at runtime and can be reso...详细分析 ▾
✓ 用途与能力
Name/description (enterprise RAG retriever) align with the delivered code and SKILL.md. The code implements embeddings (local Xenova and optional OpenAI), BM25, RRF fusion, Cross‑Encoder reranker, citation manager and CLI as advertised. The only minor mismatch: the package metadata declares no required env vars, but the code and docs reference OPENAI_API_KEY as an optional variable used when selecting OpenAI embeddings; this is reasonable (optional) but not listed in the 'required env' manifest.
ℹ 指令范围
SKILL.md instructs installing dependencies and running the provided CLI and JS API — these stay within the stated retrieval/generation scope. However the skill generates full RAG prompts and exposes an option to inject a 'systemPrompt' into the generated prompt (CitationManager.generateRAGPrompt). This is expected for RAG workflows but is a surface where adversarial or untrusted documents (or provided systemPrompt values) can influence downstream model behavior (prompt‑injection risk). The SKILL.md also reads/writes local config (.rag3-config.json) and data/ directories as part of normal operation.
✓ 安装机制
There is no custom install script in the registry entry (instruction-only), but SKILL.md recommends npm install which will fetch standard npm packages listed in package.json/package-lock.json (LanceDB, Xenova transformers, jieba, apache-arrow). Dependencies come from npm and are traceable; there are no arbitrary URL downloads or obscure extract steps in the provided manifest. Note: @xenova/transformers or runtime libraries may fetch model weights at runtime (network/cdn access) and native optional artifacts are present in package-lock.
ℹ 凭证需求
The skill does not require unrelated credentials. The only credential-like env var referenced is OPENAI_API_KEY (used optionally when provider='openai' or createEmbeddingProvider auto-detects it). That is proportionate to the described capability. It would be clearer if OPENAI_API_KEY were declared in the skill manifest as optional.
✓ 持久化与权限
The skill is not flagged always:true and uses normal local data/config paths (dbPath, .rag3-config.json, data/). It does not attempt to modify other skills or system-wide agent settings. Autonomous invocation remains allowed (platform default) but that is expected for skills of this type.
⚠ test/run-all-tests.js:22
Shell command execution detected (child_process).
安全有层次，运行前请审查代码。
运行时依赖
无特殊依赖
版本
latestv3.0.02026/3/30
Initial release of RAG 3.0 with semantic embeddings, hybrid search, reranking, and source citations
● 无害
安装命令 点击复制
官方npx clawhub@latest install rag-retriever-v3
镜像加速npx clawhub@latest install rag-retriever-v3 --registry https://cn.clawhub-mirror.com
数据来源：ClawHub ↗ · 中文优化：龙虾技能库
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