Agent skill

Databuddy

by databuddy-analytics in databuddy-analytics/Databuddy

Integrate Databuddy analytics using the SDK, REST API, or MCP.

AGPL-3.0Auto-check passedBackend & APIs

Install Databuddy

skills CLI

$ npx skills add databuddy-analytics/Databuddy --skill databuddy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install databuddy-analytics/Databuddy databuddy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/databuddy-analytics/Databuddy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.cursor/skills/databuddy .claude/skills/databuddy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "databuddy" agent skill from https://github.com/databuddy-analytics/Databuddy/tree/main/.cursor/skills/databuddy into .claude/skills/databuddy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databuddy", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/databuddy-analytics/Databuddy/tree/main/.cursor/skills/databuddy

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add databuddy-analytics/Databuddy --skill databuddy -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install databuddy-analytics/Databuddy databuddy --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/databuddy-analytics/Databuddy.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.cursor/skills/databuddy .agents/skills/databuddy && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "databuddy" agent skill from https://github.com/databuddy-analytics/Databuddy/tree/main/.cursor/skills/databuddy into .agents/skills/databuddy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databuddy", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add databuddy-analytics/Databuddy --skill databuddy -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install databuddy-analytics/Databuddy databuddy --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/databuddy-analytics/Databuddy.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.cursor/skills/databuddy .cursor/skills/databuddy && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "databuddy" agent skill from https://github.com/databuddy-analytics/Databuddy/tree/main/.cursor/skills/databuddy into .cursor/skills/databuddy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databuddy", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/databuddy-analytics/Databuddy.git --path .cursor/skills/databuddy

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add databuddy-analytics/Databuddy --skill databuddy -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install databuddy-analytics/Databuddy databuddy --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/databuddy-analytics/Databuddy.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.cursor/skills/databuddy .gemini/skills/databuddy && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "databuddy" agent skill from https://github.com/databuddy-analytics/Databuddy/tree/main/.cursor/skills/databuddy into .gemini/skills/databuddy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databuddy", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install databuddy-analytics/Databuddy databuddy

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add databuddy-analytics/Databuddy --skill databuddy -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/databuddy-analytics/Databuddy.git skills-src && mkdir -p .github/skills && cp -r skills-src/.cursor/skills/databuddy .github/skills/databuddy && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "databuddy" agent skill from https://github.com/databuddy-analytics/Databuddy/tree/main/.cursor/skills/databuddy into .github/skills/databuddy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databuddy", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add databuddy-analytics/Databuddy --skill databuddy -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install databuddy-analytics/Databuddy databuddy --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/databuddy-analytics/Databuddy.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.cursor/skills/databuddy .opencode/skills/databuddy && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "databuddy" agent skill from https://github.com/databuddy-analytics/Databuddy/tree/main/.cursor/skills/databuddy into .opencode/skills/databuddy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databuddy", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: databuddy
GitHub stars: 1.2k
Token cost: ~2.1k tokens
SKILL.md length: 543 words
Files: 7 (incl. references)
Skills in repo: 8
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

Integrate Databuddy analytics using the SDK, REST API, or MCP.

Implementing analytics tracking
SKILL.md covers External Documentation, When to Use This Skill, SDK Entry Points and Quick Start, plus 5 more sections
Calls curl; reaches api.databuddy.cc and basket.databuddy.cc; needs DATABUDDY_API_KEY
LLM observability

What it does

Databuddy is an agent skill from databuddy-analytics/Databuddy. Integrate Databuddy analytics using the SDK, REST API, or MCP. Use when implementing analytics tracking, feature flags, custom events, Web Vitals, error tracking, LLM observability, MCP agents, or querying analytics data programmatically.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `references/ai-vercel.md`, `references/api.md` and `references/core.md`).

It sits in Backend & APIs, covering Product analytics, REST APIs and Web performance. It works with Model Context Protocol, Next.js, React and Node.js. The repository describes itself as: Open-source product analytics for startups: track visitors, events, funnels, and goals without cookies, and ask Databunny, the built-in AI analyst. Uptime, feature flags, and… The licence is AGPL-3.0.

When your agent uses it

Implementing analytics tracking
LLM observability
Querying analytics data programmatically

Example prompts

“/databuddy”

Requirements

Node.js
A credential in DATABUDDY_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit 5f63610. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- api.databuddy.cc
- basket.databuddy.cc
Also links to:
- databuddy.cc
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- DATABUDDY_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Databuddy loads about 2.1k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 543 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~62
When it runs · the whole SKILL.md, loaded when a task matches: ~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from databuddy-analytics/Databuddy at commit 5f63610, republished under its AGPL-3.0 licence (© databuddy-analytics). 543 words, ~2,087 tokens.

Download SKILL.mdSave it as .claude/skills/databuddy/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.

name: databuddy
description: Integrate Databuddy analytics using the SDK, REST API, or MCP. Use when implementing analytics tracking, feature flags, custom events, Web Vitals, error tracking, LLM observability, MCP agents, or querying analytics data programmatically.
metadata.author: databuddy
metadata.version: 2.3

Databuddy

Databuddy is a privacy-first analytics platform. This skill covers both the SDK (@databuddy/sdk) and the REST API.

External Documentation

For the most up-to-date documentation, fetch: https://databuddy.cc/llms.txt

When to Use This Skill

Use this skill when:

Setting up analytics in React/Next.js/Vue applications
Implementing server-side tracking in Node.js
Adding feature flags to an application
Tracking custom events, errors, or Web Vitals
Integrating LLM observability with Vercel AI SDK
Querying analytics data via the REST API or MCP
Building MCP agents or AI-powered analytics workflows
Building custom dashboards or reports

SDK Entry Points

Import Path	Environment	Description
`@databuddy/sdk`	Browser (Core)	Core tracking utilities and types
`@databuddy/sdk/react`	React/Next.js	React component and hooks
`@databuddy/sdk/node`	Node.js/Server	Server-side tracking with batching
`@databuddy/sdk/vue`	Vue.js	Vue plugin and composables
`@databuddy/sdk/ai/vercel`	AI/LLM	Vercel AI SDK middleware for LLM analytics

Quick Start

React/Next.js

tsx

import { Databuddy } from "@databuddy/sdk/react";

export default function RootLayout({ children }) {
  return (
    <html>
      <body>
        {children}
        <Databuddy
          clientId={process.env.NEXT_PUBLIC_DATABUDDY_CLIENT_ID}
          trackWebVitals
          trackErrors
          trackPerformance
        />
      </body>
    </html>
  );
}

Node.js Server-Side

typescript

import { Databuddy } from "@databuddy/sdk/node";

const client = new Databuddy({
  clientId: process.env.DATABUDDY_CLIENT_ID,
  enableBatching: true,
});

await client.track({
  name: "api_call",
  properties: { endpoint: "/users", method: "GET" },
});

// Important: flush before process exit in serverless
await client.flush();

Feature Flags

tsx

import { FlagsProvider, useFlag, useFeature } from "@databuddy/sdk/react";

// Wrap your app
<FlagsProvider clientId="..." user={{ userId: "123" }}>
  <App />
</FlagsProvider>

// In components
function MyComponent() {
  const { on, loading } = useFeature("dark-mode");
  if (loading) return <Skeleton />;
  return on ? <DarkTheme /> : <LightTheme />;
}

LLM Analytics

typescript

import { databuddyLLM } from "@databuddy/sdk/ai/vercel";
import { openai } from "@ai-sdk/openai";

const { track } = databuddyLLM({
  apiKey: process.env.DATABUDDY_API_KEY,
});

const model = track(openai("gpt-4o"));
// All LLM calls are now automatically tracked

Key Configuration Options

Option	Type	Default	Description
`clientId`	`string`	Auto-detect	Project client ID
`disabled`	`boolean`	`false`	Disable all tracking
`trackWebVitals`	`boolean`	`false`	Track Web Vitals metrics
`trackErrors`	`boolean`	`false`	Track JavaScript errors
`trackPerformance`	`boolean`	`true`	Track performance metrics
`enableBatching`	`boolean`	`true`	Enable event batching
`samplingRate`	`number`	`1.0`	Sampling rate (0.0-1.0)
`skipPatterns`	`string[]`	—	Glob patterns to skip tracking

Common Patterns

Disable in Development

tsx

<Databuddy
  disabled={process.env.NODE_ENV === "development"}
  clientId="..."
/>

Skip Sensitive Paths

tsx

<Databuddy
  clientId="..."
  skipPatterns={["/admin/**", "/internal/**"]}
  maskPatterns={["/users/*", "/orders/*"]}
/>

Custom Event Tracking

typescript

// Browser
import { track } from "@databuddy/sdk/react";

track("purchase", {
  product_id: "sku-123",
  amount: 99.99,
  currency: "USD",
});

// Node.js
await client.track({
  name: "subscription_renewed",
  properties: { plan: "pro", amount: 29.99 },
});

Global Properties

typescript

// Browser
window.databuddy?.setGlobalProperties({
  plan: "enterprise",
  abVariant: "checkout-v2",
});

// Node.js
client.setGlobalProperties({
  environment: "production",
  version: "1.0.0",
});

REST API

Base URLs

Service	URL	Purpose
Analytics API	`https://api.databuddy.cc/v1`	Query analytics data
Event Tracking	`https://basket.databuddy.cc`	Send custom events

Authentication

Use API key in the x-api-key header:

bash

curl -H "x-api-key: dbdy_your_api_key" \
  https://api.databuddy.cc/v1/query/websites

Get API keys from: Dashboard → Organization Settings → API Keys

Query Analytics Data

bash

curl -X POST -H "x-api-key: dbdy_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "parameters": ["summary", "pages"],
    "preset": "last_30d"
  }' \
  "https://api.databuddy.cc/v1/query?website_id=web_123"

Available Query Types:

Type	Description
`summary`	Overall website metrics and KPIs
`pages`	Page views and performance by URL
`traffic`	Traffic sources and referrers
`browser_name`	Browser usage breakdown
`device_types`	Device category breakdown
`countries`	Visitors by country
`errors`	JavaScript errors
`performance`	Web vitals and load times
`custom_events`	Custom event data

Date Presets: today, yesterday, last_7d, last_30d, last_90d, this_month, last_month

Show full SKILL.md (238 more words)Show less

MCP (Model Context Protocol)

Databuddy exposes an MCP server for AI agents (Claude, Claude Code, Cursor, Windsurf, etc.) to query analytics, read investigations, and manage goals, funnels, annotations, flags, and links. Use for analytics questions, automated reports, or structured data extraction.

Endpoint: POST https://api.databuddy.cc/v1/mcp (local: http://localhost:3001/v1/mcp)

Auth: Claude and Claude Code sign in with a Databuddy account (OAuth), no key needed. Other clients, including Cursor, send an API key via x-api-key or Authorization: Bearer <key>. read:data covers analytics and reads; add manage:websites for goal, funnel, annotation, and investigation-reply writes, manage:flags for flag mutations, and organization-wide read:links (link reads) plus write:links (link mutations) for short links. Tools the key lacks scopes for are hidden from tools/list.

Tools: Use the live tools/list and the databuddy://guide resource; see https://www.databuddy.cc/docs/api/mcp for the full list. Start with list_websites, capabilities, and get_data.

Dates for get_data: a preset such as last_7d, last_30d, last_90d, today, or yesterday, or both from and to. Defaults to last_30d.

Cursor setup (mcp.json): Add a Databuddy MCP entry with the API URL and your API key in the x-api-key header.

Send Events via API

bash

curl -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "type": "custom",
    "name": "purchase",
    "properties": {
      "value": 99.99,
      "currency": "USD"
    }
  }' \
  "https://basket.databuddy.cc/?client_id=web_123"

Batch Events

bash

curl -X POST \
  -H "Content-Type: application/json" \
  -d '[
    {"type": "custom", "name": "event1", "properties": {...}},
    {"type": "custom", "name": "event2", "properties": {...}}
  ]' \
  "https://basket.databuddy.cc/batch?client_id=web_123"

Reference Documentation

For detailed documentation, see:

Core SDK Reference - Browser tracking utilities and types
React Integration - React/Next.js component and hooks
Node.js Integration - Server-side tracking with batching
Feature Flags - Feature flags for all platforms
AI/LLM Tracking - Vercel AI SDK integration
REST API Reference - Full REST API documentation

Source Code

SDK: packages/sdk/
API: apps/api/
API Docs: apps/docs/content/docs/api/

Files

SKILL.md and 6 other files (references) in .cursor/skills/databuddy of databuddy-analytics/Databuddy.

SKILL.md
references/ai-vercel.md
references/api.md
references/core.md
references/flags.md
references/node.md
references/react.md

Open the folder on GitHubat commit 5f63610

Compare with similar skills

Databuddy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Databuddy compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Databuddy this skilldatabuddy-analytics/Databuddy	1.2k	—	~2.1k	Automated safety check: Pass	AGPL-3.0	yesterday
Domscribepatchorbit/domscribe	193	—	~3.3k	Automated safety check: Notes	MIT	1 mo ago
Add React Analyticsgotempsh/temps	826	—	~2.7k	Automated safety check: Pass	Apache-2.0	today
OpenAPI to MCP Servermcp-use/mcp-use	11k	—	~5.2k	Automated safety check: Pass	Apache-2.0	today
Nextcrmpdovhomilja/nextcrm-app	712	—	~3.3k	Automated safety check: Pass	MIT	3 days ago
Scaffold ProjectMarve10s/Better-Fullstack	752	—	~716	Automated safety check: Pass	MIT	today

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Works with

Questions about Databuddy

What does Databuddy do?

Integrate Databuddy analytics using the SDK, REST API, or MCP. Databuddy is an agent skill from databuddy-analytics/Databuddy. Integrate Databuddy analytics using the SDK, REST API, or MCP.

When should I use Databuddy?

Databuddy fits situations like: implementing analytics tracking; LLM observability; querying analytics data programmatically.

How do I install Databuddy in Claude Code?

Run `npx skills add databuddy-analytics/Databuddy --skill databuddy -a claude-code`. Or copy the skill folder (.cursor/skills/databuddy in databuddy-analytics/Databuddy) into .claude/skills/databuddy in your project. Claude Code loads it when a task matches its description.

How do I install Databuddy in Codex?

Run `npx skills add databuddy-analytics/Databuddy --skill databuddy -a codex`. Or copy the skill folder (.cursor/skills/databuddy in databuddy-analytics/Databuddy) into .agents/skills/databuddy in your project. Codex loads it when a task matches its description.

Can I use Databuddy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add databuddy-analytics/Databuddy --skill databuddy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/databuddy, .gemini/skills/databuddy, .github/skills/databuddy and .opencode/skills/databuddy in your project.

What does Databuddy need to run?

Going by SKILL.md and its folder, Databuddy needs the command-line tools its instructions call (curl) and credentials named DATABUDDY_API_KEY. Our summary lists: Node.js; A credential in DATABUDDY_API_KEY.

Does Databuddy access the network?

SKILL.md names 3 domains. In commands or code: api.databuddy.cc and basket.databuddy.cc; the agent is likely to contact these when it follows the instructions. As links in the text: databuddy.cc. This is read from the text; nothing was executed.

Is Databuddy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Databuddy use?

Databuddy is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Databuddy use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.4k tokens, read only when the agent opens those files.

What are the alternatives to Databuddy?

Skills that share tags, products or a category with Databuddy: Domscribe (patchorbit/domscribe, 193 stars), Add React Analytics (gotempsh/temps, 826 stars), OpenAPI to MCP Server (mcp-use/mcp-use, 11k stars) and Nextcrm (pdovhomilja/nextcrm-app, 712 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Databuddy?

databuddy-analytics (a GitHub organization) maintains it in databuddy-analytics/Databuddy, which has 1,177 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on October 7, 2026.

Source: databuddy-analytics/Databuddy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Databuddy

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Databuddy

External Documentation

When to Use This Skill

SDK Entry Points

Quick Start

React/Next.js

Node.js Server-Side

Feature Flags

LLM Analytics

Key Configuration Options

Common Patterns

Disable in Development

Skip Sensitive Paths

Custom Event Tracking

Global Properties

REST API

Base URLs

Authentication

Query Analytics Data

MCP (Model Context Protocol)

Send Events via API

Batch Events

Reference Documentation

Source Code

Files

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