← 返回 Skills 市场
redredrrred

2026-3-22dataset

作者 redredrrred · GitHub ↗ · v1.0.5 · MIT-0
cross-platform ✓ 安全检测通过
122
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install 2026-3-22dataset
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...
使用说明 (SKILL.md)

RAGFlow Dataset And Retrieval

Use only the bundled scripts in scripts/. Prefer --json so returned fields can be relayed exactly. Follow reference.md for all user-facing output.

Use This Skill When

  • the user wants to create, list, inspect, update, or delete RAGFlow datasets
  • the user wants to upload, list, update, or delete documents in a dataset
  • the user wants to start parsing, stop parsing, or check parse progress
  • the user wants to retrieve chunks from one or more datasets
  • the user wants to list configured RAGFlow models

Core Workflow

  1. Resolve the target dataset or document IDs first.
  2. Run the matching script from scripts/.
  3. Use --json unless a script only needs a simple text response.
  4. Return API fields exactly; do not guess missing details.

Common commands:

python3 scripts/datasets.py list --json
python3 scripts/datasets.py info DATASET_ID --json
python3 scripts/datasets.py create "Example Dataset" --description "Quarterly reports" --json
python3 scripts/update_dataset.py DATASET_ID --name "Updated Dataset" --json
python3 scripts/upload.py DATASET_ID /path/to/file.pdf --json
python3 scripts/upload.py list DATASET_ID --json
python3 scripts/update_document.py DATASET_ID DOC_ID --name "Updated Document" --json
python3 scripts/parse.py DATASET_ID DOC_ID1 [DOC_ID2 ...] --json
python3 scripts/stop_parse_documents.py DATASET_ID DOC_ID1 [DOC_ID2 ...] --json
python3 scripts/parse_status.py DATASET_ID --json
python3 scripts/search.py "query" --json
python3 scripts/search.py "query" DATASET_ID --json
python3 scripts/search.py --dataset-ids DATASET_ID1,DATASET_ID2 --doc-ids DOC_ID1,DOC_ID2 "query" --json
python3 scripts/search.py --retrieval-test --kb-id DATASET_ID "query" --json
python3 scripts/list_models.py --json

Guardrails

  • For any delete action, list the exact items first and require explicit user confirmation before executing.
  • Delete only by explicit dataset IDs or document IDs. If the user gives names or fuzzy descriptions, resolve IDs first.
  • Upload does not start parsing. Start parsing only when the user asks for it.
  • parse.py returns immediately after the start request; use parse_status.py for progress.
  • For progress requests, use parse_status.py on the most specific scope available:
    • dataset specified: inspect that dataset
    • document IDs specified: pass --doc-ids
    • no dataset specified: list datasets first, then aggregate status across datasets
  • If a parse status result includes progress_msg, surface it directly. For FAIL, treat it as the primary error detail.
  • Use --retrieval-test only for single-dataset debugging or when the user explicitly asks for that endpoint.

Output Rules

  • Follow reference.md.
  • Use tables for 3+ items when possible.
  • Preserve api_error, error, message, and related fields exactly as returned.
  • Never fabricate progress percentages or inferred causes.
安全使用建议
This skill appears to do what it advertises: run the included Python scripts against a RAGFlow API. Before installing or enabling it, make sure RAGFLOW_API_URL points to a trusted RAGFlow server and provide only an API key you intend to allow for dataset/document operations. The skill will perform HTTP requests to that URL using the provided key, including endpoints that can list configured LLMs (list_models exposes api_base and other model metadata). The agent's SKILL.md asks you (the agent) to require explicit confirmation for deletes — verify that your agent actually prompts users for confirmation before destructive actions. If you need tighter control, restrict the API key's permissions (principle of least privilege) or use a read-only key for listing/status use cases.
功能分析
Type: OpenClaw Skill Name: 2026-3-22dataset Version: 1.0.5 The skill bundle provides a legitimate set of tools for interacting with the RAGFlow API to manage datasets and documents. The Python scripts in the `scripts/` directory (e.g., `datasets.py`, `upload.py`, `search.py`) use standard libraries like `urllib.request` to perform API operations and follow the instructions outlined in `SKILL.md`. There is no evidence of malicious intent, data exfiltration to unauthorized endpoints, or harmful prompt injection.
能力评估
✓ Purpose & Capability
Name/description match the included scripts and env vars: the bundle implements dataset create/list/info/update/delete, document upload/list/update/delete, parsing control, status, search, and model listing. Required env vars (RAGFLOW_API_URL, RAGFLOW_API_KEY) and python3 are appropriate for interacting with a RAGFlow HTTP API.
✓ Instruction Scope
SKILL.md instructs the agent to run the shipped scripts under scripts/ and to prefer --json, and the scripts perform only HTTP calls to the configured RAGFlow URL using the declared env vars. Scripts do not read unrelated system files or other environment variables, and guardrails (resolve IDs first, require explicit delete IDs) are present in policy text.
✓ Install Mechanism
No install spec is provided (instruction-only for runtime usage); scripts are shipped with the skill and executed with python3. No remote downloads or archive extraction are performed by an installer.
✓ Credentials
The only sensitive items requested are RAGFLOW_API_URL and RAGFLOW_API_KEY (primaryEnv), which are directly required to call the service the skill targets. The number and type of env vars are proportionate to the skill's functionality.
✓ Persistence & Privilege
The skill does not request always: true and does not attempt to modify other skills or system-wide agent settings. It requires no persistent installation steps or special privileges beyond running the bundled scripts.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install 2026-3-22dataset
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /2026-3-22dataset 触发
  4. 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
v1.0.5
Version 1.0.5 Changelog - Expanded documentation on usage, guardrails, and core workflow for RAGFlow dataset management. - Clarified command scripts for creating, listing, updating, deleting datasets and documents, including upload and parsing workflows. - Added strict output and deletion confirmation rules to prevent accidental data loss and ensure accurate API field reporting. - Detailed handling instructions for parsing status requests, error propagation, and retrieval query options. - Updated environment variable and dependency requirements in metadata.
元数据
Slug 2026-3-22dataset
版本 1.0.5
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

2026-3-22dataset 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 122 次。

如何安装 2026-3-22dataset？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install 2026-3-22dataset」即可一键安装，无需额外配置。

2026-3-22dataset 是免费的吗？

是的，2026-3-22dataset 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

2026-3-22dataset 支持哪些平台？

2026-3-22dataset 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 2026-3-22dataset？

由 redredrrred（@redredrrred）开发并维护，当前版本 v1.0.5。

推荐 Skills
self-improving agent
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
⬇ 463556 · by pskoett
Skill Vetter
Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
⬇ 259610 · by spclaudehome
Polymarket
Query Polymarket prediction markets. Check odds, find trending markets, search events, track price movements.
⬇ 232503 · by joelchance
Self-Improving + Proactive Agent
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...
⬇ 200551 · by ivangdavila
Github
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
⬇ 191226 · by steipete
ontology
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
⬇ 190235 · by oswalpalash

💬 留言讨论