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研究检索敏感数据github未标认证来源可访问许可证需确认审计异常

dot

Agent Skill

dot 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,002

周安装

81

GitHub Stars

公开资料未说明

下载量

629
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dot(点)
来源仓库:https://github.com/snowboard-software/getdot
仓库路径:skills/dot
安装命令:
npx skills add https://github.com/snowboard-software/getdot --skill dot
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/snowboard-software/getdot --skill dot

简介

dot 查询公司数据库,由 AI 分析师生成 SQL 与可视化结果。

  • 适用于销售、客户与财务数据洞察,支持自然语言转查询。
  • 自动解释结果并提供下一步建议,降低数据分析门槛。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 需配置数据库连接与权限,防止越权访问敏感表结构。
  • dot 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

dot — AI Data Analyst CLI

dot lets you query your company's databases through Dot, an AI data analyst. Dot writes SQL, runs queries, generates charts, and explains results.

Setup

If dot is not installed or not found in PATH:

  • macOS/Linux: curl -fsSL https://app.getdot.ai/install.sh | sh
  • Windows: irm https://app.getdot.ai/install.ps1 | iex

After installing, authenticate with dot login (opens browser).

When to use

Use dot when the user asks about:

  • Data questions: "What were sales last month?", "Show me top customers"
  • Metrics and KPIs: "What's our churn rate?", "Revenue by region"
  • SQL queries: "Write a query to find...", "Check the database for..."
  • Reports: "Generate a summary of...", "Break down..."
  • Any time they say "ask dot", "check the numbers", or "query the data"

Decision: catalog vs ask

  • If the user asks WHAT data is available, run dot catalog
  • If the question is vague (no specific metric, table, or time period), run dot catalog first to understand what's available, then ask a targeted question
  • If the user asks a specific data question, go straight to dot "..."

How to use

Discover available data first

Before asking questions, run dot catalog to see what data is available:

dot catalog

This returns instantly (no LLM call) and shows:

  • Available capabilities (SQL, visualizations, scheduled reports, text analysis)
  • Custom skills configured for the org
  • Data source connections with table counts
  • Top 50 tables sorted by usage, with descriptions and column/row counts
  • External assets (Looker dashboards, etc.)

Ask questions

Run dot via Bash with the question in quotes. Set a generous timeout — Dot runs a full AI analysis pipeline (SQL generation, execution, visualization) which takes 15-60 seconds, sometimes up to 2 minutes for complex queries:

dot "What were total sales last month?"

Follow-up questions

Every response includes a chat ID. Use --chat to continue the conversation:

dot "Now break down by region" --chat cli-m1abc2d-x4y5z6

When to bypass cache

Responses are cached permanently. Use --no-cache when:

  • The question involves "today", "right now", "latest", or "current"
  • The user says "refresh", "update", or "re-run"
  • The user seems unsatisfied with a previous answer
dot "What are today's sales numbers?" --no-cache

After receiving a response

  1. Parse the output text and present the explanation to the user
  2. If a chart PNG path is shown ("Chart saved to: /tmp/dot/...png"), READ the PNG file — you have multimodal capabilities and can describe what the chart shows
  3. If a CSV path is shown and the user needs detailed analysis, read and analyze the CSV data
  4. Present suggested follow-ups if they seem relevant to the user's goal
  5. If the user wants to continue, use --chat with the chat ID from the output

Output format

The output includes:

  • Text explanation — natural language answer to the question
  • SQL query — the exact SQL that was executed
  • Data preview — first rows as CSV-like text with column stats
  • Chart — saved as PNG to /tmp/dot/<chat-id>/ (read it — you're multimodal)
  • CSV data — saved locally for further analysis
  • Dot URL — link to the full interactive analysis in the browser
  • Suggested follow-ups — use these proactively if relevant

Multi-step analysis

You can orchestrate multi-step data analysis:

  1. Run dot catalog to understand available data
  2. Ask an initial question with dot "..."
  3. Read the CSV output for deeper analysis or custom calculations
  4. Ask follow-up questions using --chat to refine results
  5. Compare results across multiple queries

Caching

  • dot "question" — cached forever until --clear-cache
  • Follow-ups with --chat are never cached (always fresh)
  • dot catalog is never cached (already fast, no LLM)

Use --no-cache to force a fresh request, or --clear-cache to wipe all cached data.

Tips for good questions

  • Start with dot catalog to understand what tables and data are available
  • Be specific: include metric names, time periods, filters
  • One question at a time works best
  • Use follow-ups (--chat) to refine rather than asking compound questions
  • If you need a chart, say "show me a chart of..." or "visualize..."

Error: command not found

If dot is not found, tell the user to install it:

curl -fsSL https://app.getdot.ai/install.sh | sh
dot login

Error: Not authenticated

If you get "Not authenticated", the user needs to log in:

dot login

If dot login doesn't work (e.g., no browser available, SSO issues), the user can create an API token manually:

  1. Open the Dot dashboard in a browser (e.g., https://app.getdot.ai or your company's Dot URL)
  2. Go to Settings > Users > API Tokens
  3. Create a new token scoped to their user
  4. Use it with: dot login --token <TOKEN> --server <SERVER_URL>

Error: Authentication failed

If you get "Authentication failed", the token may have expired. The user needs to re-login:

dot login

Error: Connection failed

If dot can't reach the server, tell the user to check their internet connection. If using a custom server, verify the URL with dot status.

Debugging

Run dot status to check who is logged in, which server is configured, and whether the token is expired.

Examples

# See what data is available
dot catalog

# Simple question
dot "What were total sales last month?"

# With follow-up
dot "Compare to the same period last year" --chat cli-m1abc2d-x4y5z6

# Chart request
dot "Show me a chart of monthly revenue trend for the past 12 months"

# Specific filters
dot "Top 10 customers by order count in Q4 2025, US only"

# Force fresh answer (bypass cache)
dot "What were total sales last month?" --no-cache

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.06%
按下载量换算233

Claude

25.74%
按下载量换算162

Cursor

18.34%
按下载量换算115

Gemini CLI

9.28%
按下载量换算58

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

未通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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