> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unify.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Recipes

> Worked examples of the data + dashboard combination

The building blocks — [tables](/canvas/data), [tiles](/canvas/dashboards),
and [scheduled tasks](/tasks/overview) — are deliberately simple, and they
compose. Here are patterns that come up again and again, each just a
conversation away.

## Monitor an external service

**The ask:**

> "Every 15 minutes, check our status API. Log the response time and
> whether it was healthy. Give me a dashboard with uptime over the last
> week and a chart of response times — and text me if it's down twice in a
> row."

**What your assistant sets up:** a recurring task that calls the API and
appends a row per check to a table like `Data/StatusAPI/checks`; a live
dashboard with an uptime KPI card and a response-time chart reading from
that table; and an alert rule as part of the task. The table quietly
accumulates history — a month later, "how did latency trend after the
release?" is answerable from data you already have.

## A morning KPI board

**The ask:**

> "Build me a board I can check with coffee: yesterday's orders and
> revenue, the week-over-week trend, and the top five products. Refresh it
> from the store API every morning at 6."

**What it sets up:** a scheduled pull into `Data/Store/daily_sales`, plus a
dashboard of KPI cards and a trend chart. Because tiles are live, the 6am
task run is all it takes for the 8am glance to be current.

## Combine sources into one view

**The ask:**

> "Pull outstanding invoices from the accounts spreadsheet I email you each
> week, and payments from the CRM. Join them and show me who actually owes
> what — table plus a chart of overdue balances by client."

**What it sets up:** two tables fed from different sources — one extracted
from your emailed spreadsheet, one ingested from a
[connected app](/integrations/overview) — joined into a single live view.
Neither source system ever needed to know about the other.

## Keep intermediate work, don't redo it

**The ask:**

> "That customer-scoring analysis you just ran — keep the scored list
> somewhere permanent and put the distribution on my dashboard. We'll want
> to re-score monthly."

**What it sets up:** the computation's result becomes a durable table
rather than a throwaway answer, a tile visualizes it, and a monthly task
re-runs the scoring. Combined with [learning](/learning/overview), the
scoring method itself is saved too — so month two runs exactly like month
one.

## Derived and combined variables

**The ask:**

> "In the orders table, add a margin column — price minus cost — and show
> me average margin by product category as a bar chart."

**What it sets up:** a derived column computed from existing ones, and an
aggregation tile grouped by category. New columns can build on other
derived columns, so composite metrics ("margin per unit shipped, indexed
to January") are described in sentences, not formulas.

***

<Tip>
  The pattern behind every recipe is the same: **get the data flowing into
  a table, then put views on top.** If you're not sure whether something is
  possible, describe the outcome you want and let your assistant work out
  the plumbing — proposing a plan first for anything involved.
</Tip>