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joostboon merged 3 commits into from
Mar 15, 2026
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docs: PR/MR data quality review feature page #2152

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docs: add Elementary CI PR/MR review feature documentation
joostboon Mar 15, 2026
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docs: update GitLab setup - CI_JOB_TOKEN used, no GITLAB_API_TOKEN ne…
joostboon Mar 15, 2026
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docs: clarify review only runs on PRs that touch model files
joostboon Mar 15, 2026
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152 changes: 141 additions & 11 deletions docs/cloud/features/ci.mdx
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---
title: "Elementary CI"
sidebarTitle: "Elementary CI"
title: "PR / MR Data Quality Review"
sidebarTitle: "PR / MR Review"
---

### Closed beta - Elementary Pull request impact analysis!
Every time a developer changes a dbt model, there's a question no one can easily answer before merging: _is this safe to ship?_

When making changes to your data project, it can sometimes be hard to fully understand the impact of your changes and be certain that there are no unintended consequences.
Your dbt tests tell you if the code compiles. They don't tell you if the model you just refactored has been failing tests for the past week, whether it feeds a dashboard your CEO looks at every morning, or whether there's already an open incident on it that your data team is investigating.

Our impact analysis will run on every pull request in your dbt project, so that you can see the downstream impact of your changes.
You'll also be able to see if any of your dbt tests are failing or your models aren't being built successfully.
Elementary's PR / MR Review answers all of that automatically. The moment a pull request touches your dbt models, a structured comment appears with everything your team needs to make a confident merge decision — without leaving the PR.

![](/pics/cloud/pr_impact_example.png)
![Elementary PR review comment](/pics/cloud/pr_impact_example.png)

Elementary CI automations help you make changes with confidence by providing a comprehensive view before merging your pull request.
## Why it matters

## Want to join the beta?
Data quality issues are exponentially cheaper to catch before merge than after. But today, most teams have no visibility into data health at review time — reviewers check the SQL, not the data. By the time a broken model hits production, it's already in dashboards, downstream models, and stakeholder reports.

<Card title="Schedule a call" href="https://meetings-eu1.hubspot.com/joost-boonzajer-flaes/intro-call-docs" />
Elementary closes that gap by bringing live data quality context directly into the code review workflow.

<Card title="Reach out to us on Slack" href="https://elementary-data.com/community" />
## What you get on every PR

- **Test history** — pass/fail counts for each changed model over the last 7 days, so reviewers know if they're touching something that's already fragile
- **Active incidents** — any open data quality issues on those models right now, before the change lands on top of them
- **Downstream blast radius** — exactly which models, pipelines, and dashboards depend on what's changing, two levels deep
- **Health summary** — a plain-language signal on whether it's safe to merge, powered by Claude

The comment updates automatically on every new push, so the review always reflects the latest state. No noise, no duplicate comments.

## How it works

The review is powered by [Claude](https://www.anthropic.com) connected to the [Elementary MCP server](/cloud/mcp/intro). When a PR is opened or updated:

1. A CI job detects which models changed using `git diff`
2. Claude queries Elementary for live data quality context on those exact models
3. A structured Markdown summary is posted as a comment on the PR or MR

No custom scripts. No webhook setup. No infrastructure to manage. Two secrets and one file.

## Setup

### Prerequisites

- An Elementary Cloud account with the [MCP server enabled](/cloud/mcp/setup-guide)
- An [Anthropic API key](https://console.anthropic.com)

### GitHub Actions

**Step 1 — Add the workflow file**

Create `.github/workflows/elementary-review.yml` in your dbt repository:

```yaml
name: Elementary Data Quality Review

on:
pull_request:
paths:
- "models/**/*.sql"
- "models/**/*.yml"
- "dbt_project.yml"

jobs:
elementary-review:
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: write
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0

- uses: elementary-data/elementary-ci@v1
with:
anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
elementary-api-key: ${{ secrets.ELEMENTARY_API_KEY }}
```

**Step 2 — Add two repository secrets**

Go to **Settings > Secrets and variables > Actions** and add:

| Secret | Description |
|---|---|
| `ANTHROPIC_API_KEY` | Your Anthropic API key |
| `ELEMENTARY_API_KEY` | Your Elementary Cloud API key |

That's it. The review only runs on PRs that touch files matching the path filter — other PRs are ignored entirely.

<Warning>
This works for pull requests opened from branches within the same repository. GitHub does not pass repository secrets to `pull_request` workflows triggered by forks or Dependabot.
</Warning>

<Accordion title="Optional: customize the action">

| Input | Default | Description |
|---|---|---|
| `models-path` | `models/` | Path to your dbt models directory |
| `diff-filter` | `ACMR` | File changes to include: A=Added, C=Copied, M=Modified, R=Renamed |
| `claude-model` | `claude-haiku-4-5-20251001` | Claude model to use. Switch to `claude-sonnet-4-6` for deeper analysis on complex changes |
| `base-ref` | PR base branch | Branch to diff against |

```yaml
- uses: elementary-data/elementary-ci@v1
with:
anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
elementary-api-key: ${{ secrets.ELEMENTARY_API_KEY }}
models-path: "dbt/models/"
claude-model: "claude-sonnet-4-6"
```
</Accordion>

### GitLab CI

**Step 1 — Add the include to your `.gitlab-ci.yml`**

```yaml
include:
- remote: 'https://raw.githubusercontent.com/elementary-data/elementary-ci/v1/templates/mr-review.yml'
```

**Step 2 — Add two CI/CD variables**

Go to **Settings > CI/CD > Variables** and add:

| Variable | Masked | Description |
|---|---|---|
| `ANTHROPIC_API_KEY` | Yes | Your Anthropic API key |
| `ELEMENTARY_API_KEY` | Yes | Your Elementary Cloud API key |

GitLab's built-in `CI_JOB_TOKEN` is used to post the MR comment — no extra token setup needed.

The review only runs on MRs that touch files matching the changes filter — other MRs are ignored entirely.

## Troubleshooting

**No comment appears after the job runs**

Make sure both `contents: read` and `pull-requests: write` are set under `permissions` in the workflow. An explicit `permissions` block sets any unlisted scope to `none` — omitting `contents: read` causes the checkout step to fail before Elementary runs.

**`git diff` returns no changed models**

Make sure `fetch-depth: 0` is set on the checkout step. Without full git history the runner cannot compare branches and the diff will be empty.

**The comment says the MCP server is unreachable**

Verify `ELEMENTARY_API_KEY` is correctly set and the MCP server is enabled for your account. See the [MCP setup guide](/cloud/mcp/setup-guide).

<Warning>
If a model has never been synced through Elementary, the comment will note that no history is available yet. Results populate automatically after the next Elementary sync.
</Warning>
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