SignalQL

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AI prompting guide for SignalQL ​

Ask models for SignalQL first, then compile to SQL for execution—analytics intent stays explicit and reviewable.

Bad vs good prompts ​

Bad: “Write SQL for daily signups.”
Good: “Emit SignalQL for daily signups over the last 14 days on table events, then explain the result shape in plain language.”

Bad: “Query the warehouse.”
Good: “Given schema: event names [signup, …], produce SignalQL counting signup per day for the last 30 days.”

Patterns by topic ​

Retention (starter) ​

Ask for two counts or a funnel proxy (v0.1 does not encode cohort retention SQL fully in one clause):

“Generate SignalQL COUNT users for event active and separately for signup in the same window; describe how you’d compare them.”

Funnel conversion ​

“FUNNEL step_a THEN step_b FROM events DURING LAST N DAYS — include interpretation.”

Onboarding dropoff ​

“FUNNEL first onboarding events THEN activation event…”

Feature usage ​

“COUNT events WHERE event_name = feature_event AND properties.plan = …”

Segmentation ​

“COUNT users WHERE properties.KEY = value AND event_name = …”

Providing schema context ​

Paste short lists:

  • Known event_name values (from product taxonomy).
  • Important properties keys.
  • Whether user_id is present.

Link tools: MCP list_events, describe_event, get_schema.

Output format ​

Ask for:

  1. SignalQL query text.
  2. Interpretation (what rows/columns mean).
  3. Optional: mention that compiled SQL is produced by @signalql/compiler, not hand-written.