**Zero config. Zero YAML. Zero rules to write.**
Scherlok learns what "normal" looks like, then tells you when something changes.
---
## The Problem
Every data team has the same nightmare:
> A source API silently changes from **dollars to cents**. Revenue dashboards show wrong numbers for **3 weeks** before anyone notices.
>
> A column starts returning **NULLs**. A table stops updating. Row counts drop **40% on a Tuesday**. Nobody knows until the CEO asks why the report looks weird.
Current tools (Great Expectations, Soda, dbt tests) require you to **define what "correct" looks like** before you can detect what's wrong. Hundreds of rules. Dozens of YAML files. And you still miss things β because you can't write rules for problems you haven't imagined yet.
## The Solution
Scherlok takes the opposite approach: **learn first, then detect.**
```bash
scherlok connect postgres://user:pass@host/db # connect once
scherlok investigate # learn your data
scherlok watch # detect anomalies
```
Three commands. Five minutes. Done.
After five valid profiles, Scherlok learns per-metric variability from the
latest 30 profiles using robust historical baselines for volume, numeric mean
shifts, NULL rates, and distinct counts. During cold start or when history is
not usable, it keeps the conservative fixed defaults.
## What It Catches
| Anomaly | What Happened | Severity |
|---------|---------------|----------|
| **Volume drop** | Row count dropped 40% overnight | CRITICAL |
| **Volume spike** | 3x more rows than normal | WARNING |
| **Freshness alert** | Table hasn't updated in 12h (normally every 2h) | CRITICAL |
| **Schema drift** | Column removed or type changed | CRITICAL |
| **NULL surge** | NULL rate jumped from 2% to 45% | WARNING |
| **Distribution shift** | Column mean shifted 3+ standard deviations (Shewhart-style control limit) | INFO, WARNING above 5Ο |
| **Cardinality explosion** | Status column went from 5 values to 500 | CRITICAL |
Every anomaly is auto-scored: **INFO**, **WARNING**, or **CRITICAL**. No thresholds to configure.
## Works with dbt
Already running dbt? Scherlok complements `dbt test` with **automatic** anomaly detection β no rules to write.
```bash
pip install scherlok[dbt]
# After `dbt run`, point Scherlok at your project
scherlok dbt --project-dir ./my_dbt_project
```
Scherlok reads `target/manifest.json`, discovers every materialized model (`table`, `incremental`, `view`), auto-resolves the connection from your `profiles.yml`, and profiles each model:
```
Investigating 4 dbt models in ./my_dbt_project (postgres)
β stg_customers (12,345 rows)
β stg_orders (98,765 rows)
β fct_orders CRITICAL: Row count dropped 42% (98,765 β 57,283)
β dim_customers_inc (12,300 rows)
Summary: 4 profiled, 1 anomalies (1 critical, 0 warning)
```
Use it as a CI gate after `dbt run`:
```yaml
- run: dbt run --target prod
- run: scherlok dbt --project-dir . --target prod --fail-on critical
```
Or collapse both steps into one with the wrapper:
```yaml
- run: scherlok dbt-run-and-watch --project-dir . --target prod --fail-on critical
```
The wrapper runs `dbt run` by default and uses the successful model nodes recorded in
`target/run_results.json`, so partial runs profile only what dbt actually built. Use
`--build` to run `dbt build`; successful models are still profiled when a test failure
causes downstream models to be skipped on dbt's handled failure path (exit 1), while
the wrapper preserves `dbt build`'s exit code. Unhandled failures fail fast without
reading the artifact.
Both `dbt` and `dbt-run-and-watch` accept `--output json` for CI parsers β a single JSON document on stdout, nothing else.
**Supported adapters:** `postgres`, `bigquery`, `snowflake`, `mysql`, `duckdb`. For others, pass `--connection-string` explicitly.
π Full docs: [dbt integration guide β](https://github.com/rbmuller/scherlok/blob/HEAD/src/scherlok/dbt/README.md)
## dbt Package β native tests
Prefer staying inside dbt? Install Scherlok as a dbt package for native data quality tests β no Python CLI needed.
```yaml
# packages.yml
packages:
- package: rbmuller/scherlok
version: [">=1.0.0", "<2.0.0"]
```
```yaml
# schema.yml
models:
- name: fct_orders
tests:
- scherlok.volume_anomaly:
sensitivity: 3.0
- scherlok.row_count_between:
min_value: 100
columns:
- name: email
tests:
- scherlok.not_null_proportion:
max_rate: 0.01
- name: updated_at
tests:
- scherlok.recency:
days: 2
```
**Tier 1 β Instant (no setup):** `not_null_proportion`, `row_count_between`, `recency`, `unique_proportion`
**Tier 2 β Auto-learning (Shewhart control limits):** `volume_anomaly`, `null_anomaly` β require the `scherlok_metrics` model to build baseline history.
π Full docs: [dbt package README β](https://github.com/rbmuller/scherlok/blob/HEAD/models/_models.yml)
## HTML dashboard
```bash
scherlok dashboard --out report.html
```
One self-contained HTML file (~28 KB): KPIs, per-table incidents grouped with first-seen timestamps, `+`/`β`/`~` schema-drift diff, sparklines, and full anomaly history. Auto dark/light theme via `prefers-color-scheme`.
π Full docs: [dashboard guide β](https://github.com/rbmuller/scherlok/blob/HEAD/src/scherlok/dashboard/README.md)
## Use it from an AI agent (MCP)
Let Claude Code / Claude Desktop run data-quality checks directly:
```bash
pip install scherlok # scherlok-mcp ships built-in since v0.7.0
```
```json
{
"mcpServers": {
"scherlok": {
"command": "scherlok-mcp",
"env": { "SCHERLOK_CONNECTION": "postgresql://user:pass@host/db" }
}
}
}
```
The agent gets `list_tables`, `investigate`, `watch`, `status`, `history`, and `check` as tools. Credentials are resolved server-side (never passed by the model), every operation is read-only on the warehouse, and there's no arbitrary-SQL tool.
π Full docs: [MCP server guide β](https://github.com/rbmuller/scherlok/blob/HEAD/src/scherlok/mcp/README.md)
## AI-explained alerts (`--explain`)
Your alert says **what** broke. `--explain` adds **why** β and what to check next.
```bash
pip install 'scherlok[explain]'
export ANTHROPIC_API_KEY=sk-ant-...
scherlok watch --webhook https://hooks.slack.com/... --explain
```
When anomalies fire, Scherlok makes **one** Claude call for the whole batch and injects a short root-cause hypothesis into the same Slack/Discord/Teams/email/JSON alert:
