FastDatasets: A powerful tool for creating high-quality training datasets for Large Language Models

by ZhuLinsen · Agent Tool · ★ 224

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install FastDatasets

git clone https://github.com/ZhuLinsen/FastDatasets.git
pip install fastdatasets-llm

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 67/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

asynciodataset-generationdatasetsllmpython

Quick Facts

Stars224
Forks46
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score67/100
Last Updated2025-08-31
Created2025-04-25
Platformspython
Est. Tokens~198k

About FastDatasets

FastDatasets 🚀 一个强大的工具，用于为大语言模型（LLM）创建高质量的训练数据集 | Switch to English 🎯 在线体验 🚀 立即体验 FastDatasets，无需安装！ 上传你的文档，一键生成 Alpaca 格式训练数据集 - 完全免费，无需配置环境！ 主要功能 基于自由文档生成数据集 智能文档处理：支持多种格式文档的智能分割 问题生成：基于文档内容自动生成相关问题 答案生成：使用 LLM 生成高质量答案 异步处理：支持大规模文档的异步处理 多种导出格式：支持多种数据集格式导出（Alpaca、ShareGPT等） 直接SFT就绪输出：生成适用于监督微调的数据集 数据蒸馏与优化 知识蒸馏：从大模型中提取知识到训练数据集 指令扩增：自动生成指令变体，扩充训练数据 质量优化：使用 LLM 优化和提升数据质量 多格式支持：支持从多种格式的数据集进行蒸馏...

Compatible Skills

These tools work well together with FastDatasets for enhanced workflows:

  • mxcp — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)
  • SimplerLLM — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • KiCAD-MCP-Server — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • Happycapy-skills — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)
  • ai-microcore — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Agent Tool tools