AI Agent Setup

[TOC]

Context

The data team doesn’t have a standard approach for AI-assisted development — this guide fills that gap. It’s based on what’s already been working for several teammates, so everyone has a solid starting point rather than figuring it out from scratch.

Two terminal-based agents are documented here: Claude Code and OpenCode. Both are supported and neither is the team default — use whichever fits your workflow. The team’s shared skills follow the Agent Skills format, so one local clone works in either tool.

This guide covers setup for:

• Claude Code and OpenCode — terminal-based AI coding agents
• Snowflake CLI and the dbt MCP server — data access during an agent session
• MacWhisper — voice-driven prompting (optional)

Claude Code Setup

A more comprehensive guide is available in the internal handbook. The steps below are a simplified, data-team-specific version.

claude --version

jump analytics
claude

OpenCode Setup

A more comprehensive guide is available in the internal handbook. The steps below are a simplified, data-team-specific version.

curl -fsSL https://opencode.ai/install | bash

which opencode

echo 'export PATH=~/.opencode/bin:$PATH' >> ~/.zshrc && source ~/.zshrc

opencode --version

jump analytics
opencode

{
  "$schema": "https://opencode.ai/config.json",
  ...
  "default_agent": "plan"
}

Connecting to Other Applications

An agent gets more useful the more of your toolchain it can reach. The sections below cover the connections the data team relies on day to day — querying Snowflake, and awareness of the dbt project.

Snowflake CLI

Agents query Snowflake through the Snowflake CLI (snow), which is the Data Team standard for local Snowflake access. Follow that page to install and configure it.

Once snow connection test passes, both Claude Code and OpenCode can query Snowflake during a session — Claude Code by running snow sql in the terminal, OpenCode through its built-in snowflake tool.

Note: The Snowflake MCP server previously described here is no longer maintained and has been replaced by the Snowflake CLI. If you set the MCP server up earlier, there is no migration step — install the CLI and use it instead.

dbt MCP Server

The dbt MCP server gives an agent awareness of the dbt project — model structure, lineage, and node details. It is only relevant in the analytics repo.

First, make sure the dbt virtualenv is set up:

jump analytics
make run-dbt
ls .venv/bin/dbt  # Should show the dbt executable

If ls .venv/bin/dbt returns the file path, you’re good to go.

Then add the following to your ~/.zshrc, adjusting ANALYTICS_DIR if your analytics repo lives somewhere else:

# Analytics MCP Environment Variables
export ANALYTICS_DIR="$HOME/repos/analytics"
export DBT_PROJECT_DIR="$ANALYTICS_DIR/transform/snowflake-dbt"
export DBT_PATH="$DBT_PROJECT_DIR/.venv/bin/dbt"

Run source ~/.zshrc in the shell where you plan to start your agent.

Verify the connection:

Run /mcp in Claude Code or /mcps in OpenCode — the dbt server should be listed as connected.

Skills Setup

When you run an agent inside a repo, that repo’s own skills need no setup — the agent reads their frontmatter and invokes them when a task matches.

Skills shared across repos are different. They live in data-team-agentic-skills and have to be symlinked into your local skills directory before an agent can see them — including when a repo’s own skill calls one. Follow the setup steps in that repo’s README.

MacWhisper for Voice Prompting (Optional)

Typing out detailed prompts is slow. MacWhisper is a macOS voice-to-text app that makes it much faster to describe context and work through problems out loud — particularly for longer prompts where you’d otherwise spend more time typing than thinking.

It’s noticeably more accurate than built-in macOS Dictation app, and unlike cloud-based transcription it runs entirely on-device, so nothing leaves your machine.

View page source - Edit this page - please contribute.