技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.2
统计：⭐ 5 · 623 · 0 current installs · 0 all-time installs
⭐ 5
安装量（当前） 0
🛡 VirusTotal ：可疑 · OpenClaw ：良性
Package：angusthefuzz/ragflow
安全扫描（ClawHub）
- VirusTotal ：可疑
- OpenClaw ：良性
OpenClaw 评估
The skill's code, instructions, and requested environment variables are coherent with a Ragflow API client; nothing in the files suggests hidden backdoors or unrelated credential access.
目的
Name/description (Ragflow API client) match the included CLI and library. Required binary is node, which is appropriate. The functions and REST endpoints in code align with dataset management, uploads, parsing, and retrieval described in the SKILL.md.
说明范围
SKILL.md instructs the agent to use RAGFLOW_URL and RAGFLOW_API_KEY and run the provided node scripts. The code only reads the declared env vars, reads local files only when given an explicit path, and sends requests to the configured RAGFLOW_URL. There are no instructions to read unrelated system files or exfiltrate data to other endpoints.
安装机制
No install spec is provided (instruction-only), and the included code is plain JS. No external downloads or archive extraction are performed by an installer. This is low-risk from an install perspective.
证书
The SKILL.md and code require RAGFLOW_URL and RAGFLOW_API_KEY (appropriate and proportionate). However, the registry summary at the top of the report listed 'Required env vars: none' which contradicts the SKILL.md and code; this is a metadata inconsistency that should be resolved before trusting automated deployment/permission tooling.
持久
always is false and the skill does not request persistent system-wide changes or modify other skills. Autonomous invocation is allowed (platform default) but is not combined with any other elevated privileges here.
综合结论
This skill appears to be what it claims: a Node-based Ragflow API client. The primary risk is operational: the tool will upload files and send them to whatever RAGFLOW_URL you provide using the RAGFLOW_API_KEY. Only install/use it if you trust the Ragflow instance and you provide a least-privilege API key. Also note the registry metadata lists no required env vars while SKILL.md and the code require RAGFLOW_URL and RAGFLOW_API_KEY — verify tha…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Ragflow API Client」。简介：Universal client for Ragflow API enabling dataset management, document upload, …。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/angusthefuzz/ragflow/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
name: ragflow
description: Universal Ragflow API client for RAG operations. Create datasets, upload documents, run chat queries against knowledge bases. Self-hosted RAG platform integration.
version: 1.0.2
author: Ania
env:
RAGFLOW_URL:
description: Ragflow instance URL (e.g., https://rag.example.com)
required: true
RAGFLOW_API_KEY:
description: Ragflow API key (use least-privilege key, can manage datasets/upload files)
required: true
metadata:
clawdbot:
emoji: "📚"
requires:
bins: ["node"]
---
# Ragflow API Client
Universal client for Ragflow — self-hosted RAG (Retrieval-Augmented Generation) platform.
## Features
- **Dataset management** — Create, list, delete knowledge bases
- **Document upload** — Upload files or text content
- **Chat queries** — Run RAG queries against datasets
- **Chunk management** — Trigger parsing, list chunks
## Usage
```bash
# List datasets
node {baseDir}/scripts/ragflow.js datasets
# Create dataset
node {baseDir}/scripts/ragflow.js create-dataset --name "My Knowledge Base"
# Upload document
node {baseDir}/scripts/ragflow.js upload --dataset DATASET_ID --file article.md
# Chat query
node {baseDir}/scripts/ragflow.js chat --dataset DATASET_ID --query "What is stroke?"
# List documents in dataset
node {baseDir}/scripts/ragflow.js documents --dataset DATASET_ID
```
## Configuration
Set environment variables in your `.env`:
```bash
RAGFLOW_URL=https://your-ragflow-instance.com
RAGFLOW_API_KEY=your-api-key
```
## API
This skill wraps Ragflow's REST API:
- `GET /api/v1/datasets` — List datasets
- `POST /api/v1/datasets` — Create dataset
- `DELETE /api/v1/datasets/{id}` — Delete dataset
- `POST /api/v1/datasets/{id}/documents` — Upload document
- `POST /api/v1/datasets/{id}/chunks` — Trigger parsing
- `POST /api/v1/datasets/{id}/retrieval` — RAG query
Full API docs: https://ragflow.io/docs
## Examples
```javascript
// Programmatic usage
const ragflow = require('{baseDir}/lib/api.js');
// Upload and parse
await ragflow.uploadDocument(datasetId, './article.md', { filename: 'article.md' });
await ragflow.triggerParsing(datasetId, [documentId]);
// Query
const answer = await ragflow.chat(datasetId, 'What are the stroke guidelines?');
```