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Install in OpenClaw
/install rag-query
Description
Query the RAG knowledge base (Qdrant kb_main) by semantic search. Returns top-k chunks with text, doc_id, source, text_type, topic_tags.
README (SKILL.md)
rag-query
Usage
# 最简单：位置参数作为查询
node skills/rag-query/scripts/query.mjs "渗透测试流程"
# 显式传参 + 控制 top-k 和 topic-tags
node skills/rag-query/scripts/query.mjs \
--query "TCP/IP 模型" \
--top-k 5 \
--topic-tags "net_basic,protocol"
Parameters
| Param | Required | Example | Description |
|---|---|---|---|
--query |
yes* | "渗透测试流程" |
查询字符串；也可以作为第一个位置参数 |
--top-k |
no | 5 |
返回片段数量，默认 5 |
--topic-tags |
no | "net_basic,protocol" |
逗号分隔标签，用于按 topic_tags 过滤 |
--collection |
no | "kb_main" |
Qdrant collection 名称，默认 kb_main |
输出为 JSON 数组，每个元素包含 text、doc_id、source、text_type、topic_tags 字段，可直接注入 Agent 上下文使用。
Usage Guidance
This skill appears to do what it says (compute an embedding and query a Qdrant collection), but review the following before installing or running: 1) The script will send your query text to an embedding service (default: https://api.vectorengine.ai). If you don't want queries leaving your environment, do not run it or change EMBED_BASE_URL to a private endpoint. 2) The script will use EMBED_API_KEY if provided, but will silently fall back to VECTORENGINE_API_KEY or OPENAI_API_KEY if those exist in your environment — supply an explicit embedding key or unset unrelated keys to avoid accidental credential usage. 3) Confirm QDRANT_URL points to the intended (ideally private) Qdrant instance. 4) If you want the documentation to fully match behavior, ask the publisher to declare all environment variables the code reads (EMBED_BASE_URL, VECTORENGINE_API_KEY, OPENAI_API_KEY, RAG_INGEST_EMBED_MODEL, OPENAI_EMBEDDING_MODEL) or remove the undocumented fallbacks. If the author updates the SKILL.md to list these env vars explicitly (or removes the fallback use of OPENAI_API_KEY), this would reduce the unexpected credential-use concern and increase confidence.
Capability Analysis
Type: OpenClaw Skill
Name: rag-query
Version: 1.0.1
The rag-query skill bundle provides standard Retrieval-Augmented Generation (RAG) functionality, allowing an agent to perform semantic searches against a Qdrant vector database. The implementation in `scripts/query.mjs` follows the documented purpose in `SKILL.md`, using environment variables for API keys and performing legitimate network requests to embedding and vector search endpoints without any signs of malicious intent, obfuscation, or unauthorized data exfiltration.
Capability Assessment
Purpose & Capability
Name/description match the implementation: the script computes an embedding and queries a Qdrant collection. Requiring QDRANT_URL and an embedding API key is appropriate. However the code also accepts alternate env names (VECTORENGINE_API_KEY, OPENAI_API_KEY) and embedding-related envs (EMBED_BASE_URL, RAG_INGEST_EMBED_MODEL) that are not documented in SKILL.md.
Instruction Scope
SKILL.md instructs running the included Node script and only lists QDRANT_URL and EMBED_API_KEY. The script only performs embedding requests and a Qdrant /collections/.../points/search call (no file reads or broad system access). Still, it sends query text to an external embedding API (default https://api.vectorengine.ai) and will use other env vars if present—this diverges from the documented instruction scope.
Install Mechanism
No install spec (instruction-only). The script requires Node on PATH, which matches the declared required binary. Nothing is downloaded or installed by the skill itself.
Credentials
SKILL.md declares QDRANT_URL and EMBED_API_KEY, which are reasonable. The script also reads VECTORENGINE_API_KEY, OPENAI_API_KEY, EMBED_BASE_URL, RAG_INGEST_EMBED_MODEL, and OPENAI_EMBEDDING_MODEL as fallbacks/defaults. In particular, an existing OPENAI_API_KEY in the environment would be used silently as the embedding key if EMBED_API_KEY is not set, which is an undeclared and potentially surprising use of sensitive credentials.
Persistence & Privilege
always is false and the skill does not claim to persist or modify other agent/system configs. It runs only when invoked; no elevated or persistent privileges are requested.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install rag-query - After installation, invoke the skill by name or use
/rag-query - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Updated documentation with improved usage instructions and parameter descriptions.
- Example commands and explanations are now provided in Chinese.
- Clarified output format and usage in agent contexts.
- No code changes; this update is documentation only.
v1.0.0
Initial release of rag-query.
- Enables semantic search queries over the RAG knowledge base (Qdrant kb_main).
- Returns top-k chunks with metadata: text, doc_id, source, text_type, and topic_tags.
- CLI options: --query (required), --top-k, --topic-tags, --collection.
- Requires environment variables QDRANT_URL and EMBED_API_KEY.
Metadata
Frequently Asked Questions
What is rag-query?
Query the RAG knowledge base (Qdrant kb_main) by semantic search. Returns top-k chunks with text, doc_id, source, text_type, topic_tags. It is an AI Agent Skill for Claude Code / OpenClaw, with 473 downloads so far.
How do I install rag-query?
Run "/install rag-query" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is rag-query free?
Yes, rag-query is completely free (open-source). You can download, install and use it at no cost.
Which platforms does rag-query support?
rag-query is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created rag-query?
It is built and maintained by Seal-Re (@seal-re); the current version is v1.0.1.
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