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ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Passed 22 of 22 test cases with the skill, and 21 of 22 without it.
1 fixed21 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Write a ClickHouse SQL query computing user retention metrics (day 0, day 1, day 7, day 30 activity counts) relative to each user's signup date.
| Checked | Without skill | With skill |
|---|---|---|
| Uses dateDiff function to evaluate elapsed days between signup and activity dates | Missed | Passed |
| Uses countIf function for calculating day-specific user retention numbers | Missed | Passed |
The instructions your model reads when the skill loads.
ClickHouse-specific patterns for high-performance analytics and data engineering.
ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP). It's optimized for fast analytical queries on large datasets.
Key Features:
CREATE TABLE markets_analytics (
date Date,
market_id String,
market_name String,
volume UInt64,
trades UInt32,
unique_traders UInt32,
avg_trade_size Float64,
created_at DateTime
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
test cases passed, vs 21 / 22 without
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