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在 OpenClaw 中安装
/install ragflow-skill
功能描述
Use for RAGFlow dataset tasks: create, list, inspect, update, or delete datasets; upload, list, update, or delete documents; start or stop parsing; check par...
安全使用建议
This skill appears to be a straightforward client for a RAGFlow HTTP API. Before installing: (1) Ensure RAGFLOW_API_URL points to a trusted RAGFlow instance; (2) limit the RAGFLOW_API_KEY scope/permissions and rotate it if possible; (3) be cautious when asking the agent to upload files — the scripts will read and transmit any local file you provide or reference via @path, so do not pass sensitive local files unless intended; (4) the SKILL.md requires explicit confirmation for deletes, but verify your operational workflow enforces that; (5) if you do not want the agent to call this skill autonomously, disable autonomous invocation for this skill in your agent settings.
功能分析
Type: OpenClaw Skill
Name: ragflow-skill
Version: 1.0.8
The skill bundle provides a legitimate set of tools for interacting with the RAGFlow API to manage datasets, documents, and retrieval tasks. The scripts (e.g., `upload.py`, `search.py`, `datasets.py`) use standard Python libraries like `urllib` for network communication and include appropriate error handling and input validation. While some scripts allow reading local files via a `@path` syntax for configuration updates, this behavior is consistent with the stated purpose of the tool and does not show evidence of malicious intent or hidden data exfiltration.
能力评估
Purpose & Capability
Name/description match the included scripts: dataset CRUD, document upload/update/delete, parsing control, status checks, search, and listing configured models. Declared requirements (python3, RAGFLOW_API_URL, RAGFLOW_API_KEY) are appropriate and proportionate for an HTTP API client to a RAGFlow service.
Instruction Scope
SKILL.md directs the agent to run only the bundled scripts and to prefer --json. The scripts operate against the declared RAGFLOW_API_URL and use the API key for Authorization. The scripts legitimately read files for uploads and accept @path JSON inputs for update operations — this is expected for upload/update behavior but means the skill can read and transmit any local file the agent is asked to upload or pass as @file. Guardrails require explicit confirmation for deletes, which is appropriate.
Install Mechanism
No install spec; this is an instruction+script bundle that requires python3 on PATH. Nothing is downloaded or written during install, lowering the installation risk.
Credentials
Only RAGFLOW_API_URL and RAGFLOW_API_KEY are required and primaryEnv is the API key — this is appropriate for communicating with a RAGFlow HTTP API. There are no unrelated secrets requested. (Note: some helper code like common.py may read runtime config from args/env or config files — the manifest references resolve_runtime_config/require_api_key but declared env requirements match the expected inputs.)
Persistence & Privilege
always:false and no install hooks are present. The skill does not request system-wide persistence or changes to other skills. The agent may invoke the skill autonomously (disable-model-invocation is false), which is the platform default; this is not combined with other red flags here.
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 ragflow-skill - 安装完成后，直接呼叫该 Skill 的名称或使用
/ragflow-skill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
v1.0.8
- Documentation has been streamlined for clarity and brevity.
- Usage instructions now focus on the main dataset, document, parsing, retrieval, and model listing operations.
- All output must follow the specifications in reference.md, with exact relay of returned fields.
- Delete actions explicitly require listing items for confirmation and only proceed with exact IDs.
- Updated sample command lines and clarified the use of --json and confirmation guardrails.
- Credentials and environment requirements are now stated more clearly.
v1.0.7
Version 1.0.7
- Updated SKILL.md for clarity, conciseness, and improved guardrails on delete and parse operations.
- Simplified output rules: always use `--json`, and relay all API fields exactly.
- Enforced output and workflow to strictly follow reference.md.
- Clarified skill activation triggers and typical usage examples.
- Added explicit requirements for credential configuration.
v1.0.6
ragflow-skill 1.0.6
- Rewrote skill usage section to focus on user intent and grouped use cases by dataset management, document operations, parsing control, knowledge retrieval, and model information
- Added strong requirements to comply with output formatting in reference.md (tables, status icons, templates, no fabricated progress)
- Clarified response handling for errors and output (must relay script/API fields exactly)
- Removed bulky trigger phrase lists in favor of succinct, language-agnostic intent mapping
- Highlighted that skill should activate based on action intent, not exact keyword or language
v1.0.5
- Removed the file `readme.md`.
- No other functional or workflow changes.
v1.0.4
- Added LICENSE and readme.md files to the repository.
- Updated documentation in SKILL.md to include instructions for repeated usage with --save-to-memory, --base-url, and --api-key-file options.
- Documented parse_status.py usage step in workflow and command examples.
- No changes to core logic; only documentation and license/readme additions.
v1.0.3
Version 1.0.3
- Rebranded skill from "ragflow-knowledge" to "ragflow-dataset-ingest" and updated naming conventions.
- Removed redundant or excessive procedural and security policy documentation.
- Added clear bilingual (English/Chinese) trigger phrase lists for each supported intent (dataset/document management, parsing, retrieval, model listing).
- Clarified and streamlined the supported workflow for invoking scripts and handling user requests.
- Removed the readme.md file.
v1.0.2
- Added a .gitignore file to the project.
- Updated SKILL.md: added an explicit env_requires section for required environment variables.
- No functional changes to code or workflow logic.
v1.0.1
Initial release with improved documentation, security notes, and output standards.
- New security and privacy section added: explains API key usage, data transmission, and environment configuration requirements.
- Renamed skill from `ragflow-dataset-ingest` to `ragflow-knowledge`.
- Added explicit instructions and examples for all key workflows: dataset management, document operations, parsing control, knowledge retrieval, and model listing.
- Reference to required output formatting added—including use of [reference.md] templates, tables with status icons, and error field handling.
- Lists all supported triggers/operations, when to use the skill, and necessary confirmation steps for delete actions.
- `.env.example` and extensive git metadata provided for initial setup guidance.
v1.0.0
Initial release of RAGFlow dataset and retrieval skill.
- Added scripts for managing datasets and documents: creation, listing, updating, deletion, and upload.
- Implemented document parsing control: start, stop, and status checking.
- Enabled search/retrieval of relevant chunks from datasets.
- Included support for listing and inspecting available LLM models.
- Supports both English and Chinese trigger phrases.
- Enforced confirmation steps for destructive actions (deletion) to prevent accidental data loss.
元数据
常见问题
RAGFlow 是什么？
Use for RAGFlow dataset tasks: create, list, inspect, update, or delete datasets; upload, list, update, or delete documents; start or stop parsing; check par... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 865 次。
如何安装 RAGFlow？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install ragflow-skill」即可一键安装，无需额外配置。
RAGFlow 是免费的吗？
是的，RAGFlow 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
RAGFlow 支持哪些平台？
RAGFlow 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 RAGFlow？
由 yingfeng（@yingfeng）开发并维护，当前版本 v1.0.8。
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