data-analysis

name	data-analysis
description	Methodology recipes for multi-step data questions over the bundled DB. Load when the user asks for a complex breakdown, comparison, or anything that needs more than one SQL pass.

Data analysis recipes

The core rules (delegating to sql-agent, parsing the artifact-id contract, artifact visibility) are already in your main instructions — this skill only contains extra patterns for non-trivial questions.

Picking a dimension when the user is vague

If the user says "show me trends" or "what's interesting in the data", pick a dimension yourself rather than asking back. Default options for the Chinook DB:

• time — invoice date by month/quarter; good for trend questions
• geography — billing country; good when user mentions regions
• genre / artist — when user mentions music
• customer — when user mentions revenue / spend

Pick the dimension that gives the cleanest story. State the dimension you picked in the first line of your reply.

Multi-step delegation pattern

For questions that need two or more SQL passes (e.g. "top 10 X then break each by Y"):

1. First task(subagent_type="sql-agent", description=...) for the filter/seed list. Parse artifact_id=art_a; columns=....
2. Second task(subagent_type="sql-agent", description=...) referencing the IDs from step 1 explicitly in the description so the sub-agent can embed them in a WHERE … IN (...) clause.
3. Process / chart in python_exec, joining the two artifacts on the shared id column.

Never try to do both passes in one SQL call — the sub-agent is allowed LIMIT 200 and the join can blow past it.

Validating before you present

Quick sanity checks after read_artifact:

• Row count matches the LIMIT you asked for, or the question intent.
• Numeric totals are plausible (no all-zero, no unexpected negatives).
• No mostly-null columns.

If something looks off, refine via another sql-agent call rather than papering over it in pandas.