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Query optimization, indexing strategies, and database performance tuning for PostgreSQL and MySQL
Passed 19 of 21 test cases with the skill, and 16 of 21 without it.
and 2 more in the full report
3 fixed17 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our multi-gigabyte events table is suffering from severe performance degradation during deep pagination and wildcard text searches. Can you draft a query optimization assessment and maintenance guide that outlines partitioning strategies for large historical tables, alternatives to deep offset pagination, and bad query patterns to avoid?
| Checked | Without skill | With skill |
|---|---|---|
| The guide explicitly instructs to run EXPLAIN ANALYZE before undertaking optimizations. | Missed | Passed |
| The guide recommends keyset or cursor pagination as the alternative to deep offset pagination. | Passed | Passed |
The instructions your model reads when the skill loads.
Always run EXPLAIN ANALYZE before optimizing. Read the output bottom-up.
-- PostgreSQL
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) SELECT ...;
-- MySQL
EXPLAIN ANALYZE SELECT ...;
Key metrics to watch:
ANALYZECREATE INDEX idx_users_email ON users (email);
CREATE INDEX idx_orders_user_date ON orders (user_id, created_at DESC);
Use for: equality, range queries, sorting. Column order matters in composite indexes: put equality columns first, then range/sort columns.
CREATE INDEX idx_orders_pending ON orders (created_at)
WHERE status = 'pending';
Use when queries always filter on a specific condition. Dramatically smaller than full indexes.
CREATE INDEX idx_products_tags ON products USING GIN (tags);
CREATE INDEX idx_docs_search ON documents USING GIN (to_tsvector('english', content));
test cases passed, vs 16 / 21 without
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