by aliyun · Agent Tool · ★ 54
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
| Stars | 54 |
| Forks | 6 |
| Language | Go |
| Category | Agent Tool |
| Quality Score | 63/100 |
| Last Updated | 2026-10-22 |
| Created | 2026-03-11 |
| Platforms | go |
| Est. Tokens | ~16k |
Data Agent Skill 一个 Agent Skill,用于通过自然语言驱动阿里云瑶池 Data Agent for Analytics 进行企业数据分析。 概述 本项目是一个 AI Agent Skill,让 AI 助手(如OpenClaw、 Claude、Qoder 等)能够调用阿里云瑶池 Data Agent,帮助企业用户通过自然语言完成数据分析任务。 核心能力 数据发现 - 查询 DMS 中的实例、库、表 数据导入 - 将 DMS 数据库导入 Data Agent Data Center 问数分析 - 发起 ASKDATA / ANALYSIS / INSIGHT 会话 文件分析 - 上传 CSV/Excel/JSON 文件并分析 会话复用 - 连接已有会话、确认计划、追问 流式输出 - 实时跟踪分析进度,自动保存报告 新版特性 (v1.7.0) APIKEY...
Looking for a data-agent-skill alternative? If you're comparing data-agent-skill with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
The evaluation benchmark on MCP servers
A Model Context Protocol (MCP) server that enables natural language queries to databases
Agent skill that generates Apple-inspired bento grid presentation cards. For Claude Code, Codex, and any AI co
NL2SQL - Ask questions in plain English, get SQL queries and results. Powered by LangGraph.
🍃🔎 MongoDB Lens: Full Featured MCP Server for MongoDB Databases
A virtual design studio for Claude Code — 27 slash commands, 13 specialist roles, full design-to-code pipeline
Explore other popular agent tool tools:
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: