How Emolysis works.

What Emolysis does, how it works on your data platform, where each part stands today, and how these docs are organized, page by page.

The fixed path from a request to a deployed pipeline: connect, describe, pick a shape, fill a template, test on DuckDB, deploy, publish.

What Emolysis installs as, on which systems, and what exists today. No build is released; the emolysis package name is only a placeholder.

A first Emolysis session, step by step: a local file, a tested gold table, a dashboard, then a tested pipeline deployed on Databricks.

The Emolysis project folder: models, pipelines, tests, the emolysis.yml project file and the local lakehouse for samples.

The ten pipeline shapes, how the agent chooses one, how shapes combine into real pipelines, and what happens when no shape fits your request.

What an Emolysis template contains, the eleven slot types, how slots are filled and validated, and how to write your own templates.

How fixtures and expected outputs prove a pipeline on your machine before deploy, when the tests run, and what they do not prove.

What a platform profile records, from dialect and table format to known traps, and how the agent uses it on every request in your project.

How you connect a Databricks workspace to Emolysis: sign-in, OAuth, workspace and compute, the catalog mirror, sampling and what a deploy makes.

Snowflake is planned after Databricks, tested only on simulated services. What the connection needs, what its profile covers and what is not decided.

Microsoft Fabric is planned after Databricks, tested only on simulated services. What the connection needs and what its profile covers.

SQL Server and PostgreSQL as sources: Emolysis reads from them and never deploys to them. What that means, the shapes they feed and what is not decided.

How a green pipeline becomes your platform's native unit: Databricks Asset Bundles, Snowflake SQL or Fabric items, with pull request gates.

From a business question to a semantic model, a tested gold table and charts. The seven chart types and how publishing works.

Publishing a dashboard as a link: a stable link with revisions, three visibility settings fixed per link, snapshot links that can expire, and revoking.

The seven chart types Emolysis dashboards use, what each takes, how numbers are combined and formatted, and how many series a chart shows.

The emo commands: init, connect, login, check, token, dashboard refresh, mcp, doctor and run, with exit codes, skills and how emo fits into CI.

Using emo mcp from an editor or coding agent that supports MCP: configuration, tool names, signing in with your Emolysis account and errors.

How editors and coding agents that support MCP use Emolysis: setup buttons, ready-made configs and skills, and what your tool sends to its model.

Lint, golden tests and a cost estimate on every pull request, with emo check in your own CI; the GitHub App, which adds a preview, is planned.

Bring your own key from the model provider you choose, on any plan, or use a paid plan's model usage. What each means for cost and for your data.

Exactly what leaves your machine when you use Emolysis, and when: prompts, published dashboards, sign-in and the GitHub App.

The nine Emolysis error codes and what to do about each, plus common problems with sign-in, tests, deploys, links and MCP.

The nine error codes every Emolysis surface returns, what each one means, the usual causes, and what to do about it when one appears.