Rill Agent Skills
Rill ships built-in agent skills that teach AI coding agents how to develop Rill projects. Because Rill projects are defined as YAML and SQL files, they are a natural fit for agentic development: the skills give your agent detailed knowledge of Rill's project structure, resource types, and development workflow, so it can build connectors, models, metrics views, and dashboards on your behalf.
A single command generates everything your agent needs:
rill init --agent all
The skills work with Claude Code and Cursor out of the box, and ship in a tool-agnostic format for AGENTS.md-compatible agents such as OpenAI Codex, Gemini CLI, and GitHub Copilot. Agents that also support the Agent Skills convention load the detailed skills automatically; for others, the skill files are plain Markdown that you can point the agent at directly. See the Installation guide for setup instructions for each tool.
Agent skills and the Rill MCP Server are complementary. The skills teach your agent how to build a Rill project — resource types, YAML schemas, and best practices. The MCP server gives it live access to your running project — resource status, table schemas, query results, and file operations. rill init --agent sets up both: it generates the skill files and an MCP configuration that connects your agent to Rill Developer's local MCP server.
What you can do
With agent skills installed, your AI agent can:
- Connect to data sources like S3, GCS, BigQuery, and Snowflake
- Create and refactor SQL and YAML models, including incremental and partitioned models
- Define metrics views with measures, dimensions, and access policies
- Build explore and canvas dashboards
- Configure project-wide settings in
rill.yamland custom themes - Inspect resource status, debug parse errors, and fix reconciliation failures
- Run analytical queries against your metrics views and summarize the results
Supported tools
| Tool | Format | Generated files |
|---|---|---|
| Claude Code | Claude skills | .claude/CLAUDE.md, .claude/skills/, .mcp.json |
| Cursor | Cursor rules | .cursor/rules/, .cursor/mcp.json |
| Other agents (Codex, Gemini CLI, Copilot, ...) | AGENTS.md | AGENTS.md, .agents/skills/, .mcp.json |
Example prompts
Once installed, the skills activate automatically based on your request. Ask naturally:
"Connect to the parquet file at gs://rilldata-public/auction_data.parquet"
"Create a model that cleans the auction data and buckets bids into price ranges"
"Create a metrics view with measures for total bids, average bid price, and win rate"
"Build an explore dashboard for the auction metrics view"
"Add a canvas dashboard with KPI cards and a time series chart"
"Why is my events model erroring?"
"What were my top domains by win rate last week?"
Related
- Agentic Quickstart — step-by-step guide to building a project with an AI agent
- Installing agent skills
- Agent skills reference
- AI Configuration — add
ai_instructionsto improve AI responses for your project - Rill MCP Server — connect AI assistants to Rill Cloud projects for data analysis