cc-skill-clickhouse-io

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

1.27x

Quality

37%

Does it follow best practices?

Impact

97%

1.27x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/cc-skill-clickhouse-io/SKILL.md

Evaluation results

98%

10%

E-Commerce Analytics Platform: ClickHouse Schema Design

ClickHouse table schema design

Criteria

Without context

With context

MergeTree engine

100%

index_granularity setting

100%

Time-based partition

100%

DATE partition key type

100%

Filtered columns in ORDER BY

100%

LowCardinality usage

100%

Enum usage

100%

Appropriate integer types

75%

ReplacingMergeTree for deduplication

100%

Avoid too many partitions

100%

Denormalized design

100%

No SELECT * in examples

100%

No FINAL in examples

100%

Marketplace Analytics: Query Layer and Real-Time Rollups

Analytics query patterns and materialized views

Criteria

Without context

With context

Indexed columns first in WHERE

100%

uniq() for distinct counts

100%

quantile() for percentiles

100%

No SELECT *

100%

No FINAL modifier

100%

Materialized view created

100%

State functions in MV

100%

Merge functions in queries

100%

toStartOfHour/Day/Month in MV

100%

No excessive JOINs

100%

AggregatingMergeTree destination

100%

94%

52%

SaaS Metrics Pipeline: Loading Data into ClickHouse

TypeScript ClickHouse data ingestion pipeline

Criteria

Without context

With context

clickhouse npm package

100%

ClickHouse constructor options

100%

Batch insert pattern

40%

100%

No per-row inserts in loop

100%

.toPromise() usage

90%

Streaming insert API

100%

Environment variable config

100%

Batch sizing for bulk load

100%

TypeScript types

100%

Periodic scheduling

Repository: sickn33/antigravity-awesome-skills
Commit: e18e63c

Evaluated: about 1 month ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

E-Commerce Analytics Platform: ClickHouse Schema Design Marketplace Analytics: Query Layer and Real-Time Rollups SaaS Metrics Pipeline: Loading Data into ClickHouse

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