Bigquery features
Automate BigQuery tasks with Claude Code. Use this plugin to generate SQL, optimize costs, design schemas, and detect anti-patterns.
npx -y skills add Ocean1346/bigquery-expert --skill bigquery-featuresAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Use 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_DISTINCT, HLL_COUNT), geography/GIS functions, BigQuery ML (CREATE MODEL), search indexes, vector search, or BI Engine. Triggers on: "UNNEST", "STRUCT", "ARRAY", "MERGE", "DECLARE", "scripting", "scheduled query", "remote function", "JSON_EXTRACT", "APPROX_COUNT", "HLL", "ST_", "CREATE MODEL", "BQML", "search index", "vector search", "BI Engine".
SKILL.md
2.9 KB, as published. Nobody here has run it
BigQuery Features
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.
Feature Quick Reference
| Feature | Use Case | Key Syntax |
|---|---|---|
| STRUCT/ARRAY | Nested data, denormalization | STRUCT<>, ARRAY<>, UNNEST() |
| MERGE | Upserts, SCD Type 2 | MERGE...WHEN MATCHED...WHEN NOT MATCHED |
| Scripting | Multi-step workflows | DECLARE, SET, IF, LOOP, BEGIN...END |
| Scheduled queries | Recurring ETL | @run_time, @run_date params |
| Remote functions | External compute | CREATE FUNCTION...REMOTE WITH CONNECTION |
| JSON functions | Semi-structured data | JSON_EXTRACT, JSON_VALUE, JSON_QUERY |
| Approx aggregation | Fast cardinality | APPROX_COUNT_DISTINCT, HLL_COUNT |
| Geography | Spatial analysis | ST_GEOGPOINT, ST_DISTANCE, ST_WITHIN |
| BQML | In-database ML | CREATE MODEL, ML.PREDICT, ML.EVALUATE |
| Search/Vector | Full-text & similarity | SEARCH(), VECTOR_SEARCH() |
| BI Engine | Sub-second dashboards | Reservation-based, auto-accelerates |
Behavioral Rules
When Explaining a Feature
For every feature question, provide all four of these:
- What it is -- concise definition and where it fits in BigQuery's architecture.
- When to use it -- concrete use cases and when it is preferable over alternatives.
- Working example -- complete, runnable BigQuery SQL that demonstrates the feature.
- Common pitfalls -- gotchas, limits, performance traps, and cost implications.
General Guidelines
- Always use BigQuery-specific syntax (backtick-quoted projects,
STRUCT<>notation,SAFE.prefix where relevant). - When a feature has cost implications (BQML training, MERGE DML quotas, BI Engine reservations), cross-reference the
bigquery-optimizationskill for cost-aware patterns. - Prefer practical patterns over theoretical explanations. Show SQL that can be copy-pasted and run.
- When multiple approaches exist (e.g., JSON_EXTRACT vs native JSON type), explain trade-offs clearly.
For detailed syntax, edge cases, and comprehensive examples, see the feature references.