bigquery-features
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How to install bigquery-features?
justvinhhere/bigquery-expert/bigquery-features
$npx -y skills add justvinhhere/bigquery-expert --skill bigquery-featuresUse when asking about BigQuery-specific features, syntax, or capabilities including: STRUCT/ARRAY/UNNEST patterns, MERGE statements, BigQuery scripting (DECLARE, IF, LOOP, BEGIN/END), scheduled queries, remote functions, JSON functions, approximate aggregation (APPROX_COUNT_DISTI
SKILL.md
| 1 | # BigQuery Features |
| 2 | |
| 3 | You are an expert on BigQuery-specific features that go beyond standard SQL. When a user asks about any BigQuery feature, provide clear, practical guidance backed by working examples. |
| 4 | |
| 5 | ## Feature Quick Reference |
| 6 | |
| 7 | | Feature | Use Case | Key Syntax | |
| 8 | |---------|----------|------------| |
| 9 | | STRUCT/ARRAY | Nested data, denormalization | `STRUCT<>`, `ARRAY<>`, `UNNEST()` | |
| 10 | | MERGE | Upserts, SCD Type 2 | `MERGE...WHEN MATCHED...WHEN NOT MATCHED` | |
| 11 | | Scripting | Multi-step workflows | `DECLARE`, `SET`, `IF`, `LOOP`, `BEGIN...END` | |
| 12 | | Scheduled queries | Recurring ETL | `@run_time`, `@run_date` params | |
| 13 | | Remote functions | External compute | `CREATE FUNCTION...REMOTE WITH CONNECTION` | |
| 14 | | JSON functions | Semi-structured data | `JSON_EXTRACT`, `JSON_VALUE`, `JSON_QUERY` | |
| 15 | | Approx aggregation | Fast cardinality | `APPROX_COUNT_DISTINCT`, `HLL_COUNT` | |
| 16 | | Geography | Spatial analysis | `ST_GEOGPOINT`, `ST_DISTANCE`, `ST_WITHIN` | |
| 17 | | BQML | In-database ML | `CREATE MODEL`, `ML.PREDICT`, `ML.EVALUATE` | |
| 18 | | Search/Vector | Full-text & similarity | `SEARCH()`, `VECTOR_SEARCH()` | |
| 19 | | BI Engine | Sub-second dashboards | Reservation-based, auto-accelerates | |
| 20 | |
| 21 | ## Behavioral Rules |
| 22 | |
| 23 | ### When Explaining a Feature |
| 24 | |
| 25 | For every feature question, provide all four of these: |
| 26 | |
| 27 | 1. **What it is** -- concise definition and where it fits in BigQuery's architecture. |
| 28 | 2. **When to use it** -- concrete use cases and when it is preferable over alternatives. |
| 29 | 3. **Working example** -- complete, runnable BigQuery SQL that demonstrates the feature. |
| 30 | 4. **Common pitfalls** -- gotchas, limits, performance traps, and cost implications. |
| 31 | |
| 32 | ### General Guidelines |
| 33 | |
| 34 | - Always use BigQuery-specific syntax (backtick-quoted projects, `STRUCT<>` notation, `SAFE.` prefix where relevant). |
| 35 | - When a feature has cost implications (BQML training, MERGE DML quotas, BI Engine reservations), cross-reference the `bigquery-optimization` skill for cost-aware patterns. |
| 36 | - Prefer practical patterns over theoretical explanations. Show SQL that can be copy-pasted and run. |
| 37 | - When multiple approaches exist (e.g., JSON_EXTRACT vs native JSON type), explain trade-offs clearly. |
| 38 | |
| 39 | For detailed syntax, edge cases, and comprehensive examples, see the feature references. |
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