AugmentClaude
ETLSQL

ClickHouse Analytics

By arabicapp· arabicapp/everything-claude-code· 0

Optimize ClickHouse queries and design high-performance analytical databases.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.
    Install
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    This copies the whole skill folder into ~/.claude/skills/clickhouse-io-arabicapp/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
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  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

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

What this skill does

ClickHouse is a columnar database management system (DBMS) optimized for online analytical processing (OLAP) on large datasets. This skill covers ClickHouse-specific patterns for designing high-performance analytical databases, optimizing queries, and building efficient data pipelines. It includes best practices for table design, query optimization, data ingestion, materialized views, and real-time analytics.

What it does

  • Table design patterns – Configure MergeTree, ReplacingMergeTree, and AggregatingMergeTree engines with partitioning and primary key strategies for analytical workloads.
  • Query optimization – Write efficient filtering, aggregation, and window function queries that leverage ClickHouse's columnar storage and index structures.
  • Data ingestion – Implement bulk inserts, streaming inserts, and change data capture (CDC) patterns for continuous data ingestion from source systems.
  • Materialized views – Create real-time aggregations using materialized views with state functions (sumState, countState, uniqState) to maintain pre-aggregated metrics.
  • Performance monitoring – Query system tables to identify slow queries, track table sizes, and monitor merge operations.
  • Analytics patterns – Build time-series analysis, retention cohorts, funnel analysis, and ETL pipelines using ClickHouse-specific functions like quantile, uniq, and dateiff.

How to use it

Describe your analytical workload—table structure, query patterns, data volume, or performance issues—and this skill will provide ClickHouse schema designs, optimized SQL examples, best practices for partitioning and sorting keys, and guidance on materialized views and data pipeline architecture. Ask for help optimizing specific queries, designing tables for your use case, or implementing real-time aggregations.

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