clickhouse-best-practices

Overview of clickhouse-best-practices skill

What this skill is for

The clickhouse-best-practices skill is a ClickHouse-specific review and guidance package for schema design, query tuning, insert strategy, and agent connectivity. It is most useful when you need more than generic SQL advice and want recommendations grounded in ClickHouse storage, merge-tree behavior, and query execution patterns.

Who should use it

Use the clickhouse-best-practices skill for Database Engineering work on ClickHouse schemas, slow queries, ingestion pipelines, MVs, and agent-driven analysis. It is a strong fit when you are deciding how to model data, not just fixing one query.

Why it is different

The main value is its rule-based structure: the repository contains atomic checks that can be cited directly in answers, which reduces guesswork and makes recommendations easier to justify. That matters because ClickHouse tradeoffs are often non-obvious, especially around ORDER BY, partitioning, joins, and mutation-heavy workflows.

How to Use clickhouse-best-practices skill

Install and activate it

For the clickhouse-best-practices install step, add the skill from the ClickHouse agent skills repo and then load it in your agent workflow:

npx skills add ClickHouse/agent-skills --skill clickhouse-best-practices

If your environment uses a different skill manager, keep the repo path and skill slug the same so the agent can resolve the right folder.

Give it the right input

The clickhouse-best-practices usage pattern works best when you provide the table shape, query pattern, data volume, and goal in one prompt. Good inputs name the engine, cardinality, filters, write rate, and pain point. For example: “Review this MergeTree table for dashboard queries filtered by event_date and tenant_id; current table has 2B rows, daily inserts, and slow joins.”

Start with the right files

A practical clickhouse-best-practices guide begins with SKILL.md, then README.md, AGENTS.md, and metadata.json, followed by the specific rule files under rules/. For schema work, read rules/schema-pk-plan-before-creation.md, rules/schema-partition-start-without.md, and rules/schema-types-avoid-nullable.md first. For query work, prioritize rules/query-join-filter-before.md and rules/query-join-choose-algorithm.md.

Use a workflow, not a vague prompt

A strong prompt asks for a review against applicable rules, not a free-form opinion. Example: “Assess this schema against clickhouse-best-practices, cite the relevant rule filenames, identify high-risk design choices, and propose the smallest changes that improve scan efficiency.” That gets you closer to an actionable engineering review instead of a generic best-practices summary.

clickhouse-best-practices skill FAQ

Is this only for experts?

No. It is beginner-friendly if you already know your ClickHouse use case and can share table definitions or query examples. The skill helps most when users know the problem but want the right ClickHouse pattern, not when they need a tutorial on SQL basics.

When should I not use it?

Do not rely on clickhouse-best-practices for unrelated SQL systems or for broad analytics advice that is not ClickHouse-specific. If you are not using ClickHouse, the rules about merge trees, sparse indexes, and partitioning may mislead more than they help.

How is it better than a normal prompt?

A normal prompt often returns generic advice like “add an index” or “denormalize the data.” This skill is more useful because it pushes the agent to check the applicable rule set, which is especially important for ClickHouse best practices around ORDER BY, joins, batch inserts, and mutation avoidance.

Does it support agent-to-ClickHouse workflows?

Yes. The repository includes agent connectivity and discovery guidance, so clickhouse-best-practices is also relevant when an agent needs to connect to ClickHouse safely, inspect schema before querying, and avoid unbounded scans.

How to Improve clickhouse-best-practices skill

Provide concrete table and workload details

The best results come from inputs that include ENGINE, primary key or sort key, partitioning, row counts, insert frequency, and the exact query shape. For example, “table has 400M rows, partitions by month, query filters by tenant_id and created_at, joins on user_id” is much more useful than “make it faster.”

Ask for rule-cited recommendations

If you want higher-trust output from clickhouse-best-practices, ask the agent to name the rule files it used and to separate must-fix issues from nice-to-have improvements. This helps you see whether the answer is based on schema rules, query rules, insert rules, or agent-safety rules.

Iterate from diagnosis to redesign

Common failure mode: asking for a single SQL rewrite when the real fix is schema-level. If the first answer still scans too much data or depends on FINAL, iterate by sharing the table DDL, the slow query, and the expected access pattern. That lets the skill move from surface tuning to a design that fits ClickHouse storage behavior.

Use it to challenge assumptions early

For Database Engineering, the biggest gains often come from catching bad defaults before production: nullable columns that are not needed, partitions with too many values, joins done before filtering, or inserts that are too small. The clickhouse-best-practices skill is strongest when you use it during design review, not only after a performance incident.