Slash Command

/data-query

Write or explain SQL and analytics queries for product data. Use when a PM needs to pull product metrics, understand user behavior, or validate analytics without waiting for a data analyst.

Invocation

How this command is triggered — by the user, by Claude, or both

Slash command

/pm-analytics:data-query [write | explain] [query description or SQL]

Model invocable

No pre-commands

Context Preview

The summary Claude sees in its command listing — used to decide when to auto-load this command

Writes or explains SQL and analytics queries for product use cases.

## Routing

- **`write [description]`**: write a query for the described question.
- **`explain [SQL]`**: explain what an existing query does in plain language.
- **No argument**: ask questions below first.

## Questions (no argument)



## Output (write)

**Query: [Description]**



**What this returns**
Plain-language description of the result set — columns, rows, and what the numbers represent.

**How to interpret**
What to look for in the results. Common gotchas or caveats.

**Variations**
1–2 common follow-up queries ...

Command Content

71 lines · ~481 tokens

Stats

Parent stars0

MaintenanceGood

Last CommitMay 11, 2026

Actions

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Stats

Actions

Routing

write [description]: write a query for the described question.
explain [SQL]: explain what an existing query does in plain language.
No argument: ask questions below first.

Questions (no argument)

Before I write the query, a few questions:

1. Write a new query or explain an existing one?
2. What question are you trying to answer?
3. What tables or data do you have access to? (schema, table names, key columns)
4. What analytics tool or SQL dialect? (BigQuery / PostgreSQL / Redshift / Snowflake / Amplitude / Mixpanel)
5. Any time range or filter constraints?

Answer as many as you can.

Output (write)

Query: [Description]

-- [Brief comment on what this query answers]
SELECT
  ...
FROM
  ...
WHERE
  ...
GROUP BY
  ...
ORDER BY
  ...

What this returns Plain-language description of the result set — columns, rows, and what the numbers represent.

How to interpret What to look for in the results. Common gotchas or caveats.

Variations 1–2 common follow-up queries (segment by cohort, filter to specific plan, etc.).

Output (explain)

Query explanation: [source]

What this query does in plain English:

[what data it reads from]
[what it filters]
[what it groups/aggregates]
[what the result represents]

Potential issues Performance concerns, logic edge cases, or results that might surprise you.

Style

Queries should be readable — comments where the logic isn't obvious, CTEs instead of deeply nested subqueries. A query a PM can read is a query a PM can modify.