القائمة
Product analytics rules: calendar spine cross-joins, date boundary caps, event type pivoting, first-run NULL behavior.
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npx skills add https://github.com/SignalPilot-Labs/SignalPilot --skill domain-productيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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Populate the knowledge base from dbt project research. Proposes entries across all 6 categories at org, project, and connection scopes.
Load when working with Xata Postgres branches: forking a branch, building or testing dbt models on a branch, wiring dbt to a branch's credentials, diffing two branches, or the pre-merge impact report. Covers create_xata_branch, delete_xata_branch, get_dbt_profile, xata_branch_diff, schema_diff_branches, and pgroll migrations.
Load at Step 1 before exploring the project. Covers output shape inference, incremental model handling, and what to trust in YML.
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عرض SKILL.md
| name | domain-product |
| description | Product analytics rules: calendar spine cross-joins, date boundary caps, event type pivoting, first-run NULL behavior. |
When aggregating usage or event metrics, drive FROM the fact/event table (events, sessions, pageviews), not the dimension table (users, features, pages). Users with zero activity MUST NOT appear in activity reports - they have no data to aggregate.
Daily metrics models that cross-join a calendar spine with entities (users, features, pages) MUST cap the join at current_date - generating rows beyond today produces phantom future rows with zero metrics that inflate row counts.
If the YML description says "from creation date to current date," add AND spine.date_day >= entity.created_at::date AND spine.date_day <= current_date to the join condition - both bounds are required.
NEVER cross-join without a date cap - unbounded spines grow indefinitely and break row count expectations.
When an entity has a created_at or equivalent timestamp, the spine MUST start from that date, not from the earliest date in the spine - starting earlier produces rows before the entity existed.
When an events table has a type or event_name column, pivot distinct values into separate metric columns - each event type becomes a count_<type> column in the output.
Do NOT filter the events table to a single type before pivoting - all types must appear in the output as separate columns, using conditional aggregation (COUNT(CASE WHEN type = 'x' THEN 1 END)).
Read the YML or a sibling model to confirm the exact set of event types expected - inventing column names from partial data causes schema mismatches.
On first build with no prior state, rolling window columns (7-day avg, 30-day sum) MUST be NULL for the first N-1 periods - this is correct behavior, not a bug.
Do NOT substitute 0 for NULL in rolling window columns - NULL means "insufficient history," 0 means "activity was zero."
Period-over-period columns follow the same rule: NULL on first build. See dbt-workflow "Incremental Models and Period-Over-Period Columns" for the full rule.
