Clickhouse Io

Activates when you're working with ClickHouse for analytics workloads, table design, or query optimization. Covers the practical stuff: choosing the right MergeTree engine for your use case, writing efficient aggregations with ClickHouse-specific functions like quantile and uniq, setting up materialized views for real-time stats, and batch insertion patterns that won't kill performance. The examples are solid, real-world patterns like retention analysis, funnel queries, and partition pruning strategies. Honestly most useful if you're migrating from a traditional RDBMS to ClickHouse or building dashboards on large datasets and need to understand how column-oriented analytics actually work in practice.

skills.sh installs: 5.5K
GitHub stars: 300.3K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add affaan-m/ecc --skill clickhouse-io --agent claude-code

Installs into .claude/skills of the current project.

Files

ClickHouse Analytics Patterns

ClickHouse-specific patterns for high-performance analytics and data engineering.

When to Activate

Designing ClickHouse table schemas (MergeTree engine selection)
Writing analytical queries (aggregations, window functions, joins)
Optimizing query performance (partition pruning, projections, materialized views)
Ingesting large volumes of data (batch inserts, Kafka integration)
Migrating from PostgreSQL/MySQL to ClickHouse for analytics
Implementing real-time dashboards or time-series analytics

Overview

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: