Superset can answer almost any question about your data, but getting there means clicking through a lot of screens: database connections, datasets, charts, dashboards, saved queries. On your OpenSysLab server the vibe-code agent can reach the app config, the metadata database, and the Superset CLI, so most of that clicking becomes a typed request you can review. The jobs below are the ones owners actually delegate week to week: they are concrete, they finish fast, and you can see the result before anything goes live. Here are ten everyday jobs worth delegating first.

1. Connect a new database

Every Superset answer starts with a database connection. Hand the agent the connection details and it registers the database in Superset, tests the SQLAlchemy URI, and exposes the tables you care about as datasets. Try: “Connect the orders Postgres database with a read-only user, test the connection, and add the orders, customers, and products tables as datasets.” It will also flag tables with no usable time column, which is the usual reason a dataset refuses to chart, and suggest a fix before you hit it yourself.

2. Build your first real dashboard

A pile of charts is not a dashboard. Describe the business question and let the agent assemble the layout: a time-series line for the trend, a bar chart for the ranking, big numbers for the totals. Try: “Build a sales overview dashboard from the orders dataset: weekly revenue line, top 10 products bar chart, and total orders with average order value as big numbers.” You review the grid, swap two titles, and have something presentable the same day instead of after a week of tutorials. Ask it to add a date filter at the top while it is in there, and the dashboard becomes a tool instead of a poster.

3. Save the queries people keep re-typing

SQL Lab is where analysis actually happens, and saved queries are where it gets reused. Ask for the query, watch it run against your real data, then have it saved under a name the next person will recognize. Try: “In SQL Lab, write a query that shows monthly revenue per customer for the last 12 months, save it as ‘MRR by customer’, and format the SQL.” Repeat this for the half-dozen queries your team keeps in chat history, and those numbers stop depending on who is online.

4. Clean up the chart graveyard

Superset instances rot quietly: datasets that never got a chart, charts nobody placed on a dashboard, three things with nearly the same name. The agent can read the metadata database and draft a cleanup list before touching anything. Try: “List datasets with no charts and charts that are not on any dashboard from the last six months, and propose what to archive.” You approve the list, it archives, and the chart picker stops suggesting last year’s experiments. Your metadata database also shrinks, which you will notice the next time someone searches for a chart by name.

5. Onboard a teammate properly

New-user setup spans several screens: the user, the role, default dashboards, maybe a schema restriction. Describe the person and their needs, and the agent handles the rest. Try: “Create a login for ana@company.com, give her view access to the Sales and Marketing dashboards only, and no SQL Lab.” Her first-week friction drops to a password prompt, and you did not have to remember where the permissions page hides. The same request works for a batch of hires at once: give it a list of emails and roles and it creates all the accounts, then reports back any that failed.

6. Fix a chart that broke overnight

A column renamed upstream and now a chart shows a database error instead of a number. The agent reads the error, compares the dataset definition against the actual table, and repairs the column reference. Try: “The ‘Weekly active users’ chart shows a database error, find out why and fix it.” If the column was renamed rather than dropped, the fix takes minutes, and you also get an explanation you can forward to whoever changed the schema.

7. Load a spreadsheet as a dataset

Marketing spend and headcount numbers usually arrive as CSV files. The agent can upload the file into the analytics database as a proper table, then register it as a dataset so it charts like everything else. Try: “Upload marketing_spend.csv into the analytics database as a new table, register it as a dataset, and use the month column as the time column.” One-off spreadsheets stop living as email attachments and start living next to your real data. The agent will also catch the two classic CSV problems for you: dates parsed as text and numeric columns with currency symbols baked in.

8. Clone a dashboard for another team

Your EU team wants the same view, but on EU data. The agent copies the dashboard, retargets each chart at the EU dataset, and renames the pieces so nobody charts the wrong continent by mistake. Try: “Copy the Sales overview dashboard into an EU Sales version that reads from the eu_orders dataset, and adjust the chart titles.” Done by hand this means opening every chart; done through the metadata it is faster and misses nothing.

9. Turn a one-off question into a chart

Some questions do not justify a permanent dashboard: which customers ordered in the last 90 days but never before, which products get returned most, which region grew fastest. The agent writes the SQL, shows you the table, and if the answer deserves a picture, saves the chart for later. Try: “Which customers ordered in the last 90 days but not in the 12 months before that? Show me the count by country as a bar chart.” You get the answer and an artifact you can come back to. When the same question shows up three weeks running, promote it: ask the agent to pin the chart onto a dashboard and set a refresh schedule.

10. Publish a data dictionary people will actually read

After a few months, only one person knows what each dataset really contains, and it is probably you. Have the agent interview the metadata and write it down. Try: “Write a one-page data dictionary of our datasets: what each one holds, who owns it, and how often it updates, and put it on an ‘About our data’ dashboard.” New hires stop asking you which table counts revenue, because the answer is on the dashboard list.

See it in action

Official walkthroughs from the Apache Superset team:

Worth a watch next:

• Introducing Claudette – the fastest way to develop with Claude Code on the Apache Superset Project — the Superset team’s own demo of building on the project with an AI coding agent.
• Live Demo: Preset Agent Skills — watch the everyday Superset jobs from this list being delegated to an agent.

None of these need a BI specialist on call; they need someone who knows what to ask. Start with one job, watch the agent work, and keep the review step for yourself. If the list feels useful, a preassembled server gets you there faster: Apache Superset on OpenSysLab ships with the agent already installed and connected to your data.