clickhouse-best-practices

clickhouse/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/clickhouse/agent-skills --skill clickhouse-best-practices
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summary

28 ClickHouse best practices rules organized by schema design, query optimization, and data ingestion strategy.

  • Covers three critical areas: primary key and data type selection (immutable design decisions), JOIN and query optimization, and insert batching with mutation avoidance
  • Includes 28 rules prioritized by impact, with schema design and query optimization rules marked CRITICAL due to ClickHouse's columnar storage and sparse index mechanics
  • Provides structured review procedures f
skill.md

ClickHouse Best Practices

Comprehensive guidance for ClickHouse covering schema design, query optimization, and data ingestion. Contains 28 rules across 3 main categories (schema, query, insert), prioritized by impact.

Official docs: ClickHouse Best Practices

IMPORTANT: How to Apply This Skill

Before answering ClickHouse questions, follow this priority order:

  1. Check for applicable rules in the rules/ directory
  2. If rules exist: Apply them and cite them in your response using "Per rule-name..."
  3. If no rule exists: Use the LLM's ClickHouse knowledge or search documentation
  4. If uncertain: Use web search for current best practices
  5. Always cite your source: rule name, "general ClickHouse guidance", or URL

Why rules take priority: ClickHouse has specific behaviors (columnar storage, sparse indexes, merge tree mechanics) where general database intuition can be misleading. The rules encode validated, ClickHouse-specific guidance.

For Formal Reviews

When performing a formal review of schemas, queries, or data ingestion:


Review Procedures

For Schema Reviews (CREATE TABLE, ALTER TABLE)

Read these rule files in order:

  1. rules/schema-pk-plan-before-creation.md - ORDER BY is immutable
  2. rules/schema-pk-cardinality-order.md - Column ordering in keys
  3. rules/schema-pk-prioritize-filters.md - Filter column inclusion
  4. rules/schema-types-native-types.md - Proper type selection
  5. rules/schema-types-minimize-bitwidth.md - Numeric type sizing
  6. rules/schema-types-lowcardinality.md - LowCardinality usage
  7. rules/schema-types-avoid-nullable.md - Nullable vs DEFAULT
  8. rules/schema-partition-low-cardinality.md - Partition count limits
  9. rules/schema-partition-lifecycle.md - Partitioning purpose

Check for:

  • PRIMARY KEY / ORDER BY column order (low-to-high cardinality)
  • Data types match actual data ranges
  • LowCardinality applied to appropriate string columns
  • Partition key cardinality bounded (100-1,000 values)
  • ReplacingMergeTree has version column if used

For Query Reviews (SELECT, JOIN, aggregations)

Read these rule files:

  1. rules/query-join-choose-algorithm.md - Algorithm selection
  2. rules/query-join-filter-before.md - Pre-join filtering
  3. rules/query-join-use-any.md - ANY vs regular JOIN
  4. rules/query-index-skipping-indices.md - Secondary index usage
  5. rules/schema-pk-filter-on-orderby.md - Filter alignment with ORDER BY

Check for:

  • Filters use ORDER BY prefix columns
  • JOINs filter tables before joining (not after)
  • Correct JOIN algorithm for table sizes
  • Skipping indices for non-ORDER BY filter columns

For Insert Strategy Reviews (data ingestion, updates, deletes)

Read these rule files:

  1. rules/insert-batch-size.md - Batch sizing requirements
  2. rules/insert-mutation-avoid-update.md - UPDATE alternatives
  3. rules/insert-mutation-avoid-delete.md - DELETE alternatives
  4. rules/insert-async-small-batches.md - Async insert usage
  5. rules/insert-optimize-avoid-final.md - OPTIMIZE TABLE risks

Check for:

  • Batch size 10K-100K rows per INSERT
  • No ALTER TABLE UPDATE for frequent changes
  • ReplacingMergeTree or CollapsingMergeTree for update patterns
  • Async inserts enabled for high-frequency small batches

Output Format

Structure your response as follows:

## Rules Checked
- `rule-name-1` - Compliant / Violation found
- `rule-name-2` - Compliant / Violation found
...

## Findings

### Violations
- **`rule-name`**: Description of the issue
  - Current: [what the code does]
  - Required: [what it should do]
  - Fix: [specific correction]

### Compliant
- `rule-name`: Brief note on why it's correct

## Recommendations
[Prioritized list of changes, citing rules]

Rule Categories by Priority

Priority Category Impact Prefix Rule Count
1 Primary Key Selection CRITICAL schema-pk- 4
2 Data Type Selection CRITICAL schema-types- 5
3 JOIN Optimization CRITICAL query-join- 5
4 Insert Batching CRITICAL insert-batch- 1
5 Mutation Avoidance CRITICAL insert-mutation- 2
6 Partitioning Strategy HIGH schema-partition- 4
7 Skipping Indices HIGH query-index- 1
8 Materialized Views HIGH query-mv- 2
9 Async Inserts HIGH insert-async- 2
10 OPTIMIZE Avoidance HIGH insert-optimize- 1
11 JSON Usage MEDIUM schema-json- 1

Quick Reference

Schema Design - Primary Key (CRITICAL)

  • schema-pk-plan-before-creation - Plan ORDER BY before table creation (immutable)
  • schema-pk-cardinality-order - Order columns low-to-high cardinality
  • schema-pk-prioritize-filters - Include frequently filtered columns
  • schema-pk-filter-on-orderby - Query filters must use ORDER BY prefix

Schema Design - Data Types (CRITICAL)

  • schema-types-native-types - Use native types, not String for everything
  • schema-types-minimize-bitwidth - Use smallest numeric type that fits
  • schema-types-lowcardinality - LowCardinality for <10K unique strings
  • schema-types-enum - Enum for finite value sets with validation
  • schema-types-avoid-nullable - Avoid Nullable; use DEFAULT instead

Schema Design - Partitioning (HIGH)

  • schema-partition-low-cardinality - Keep partition count 100-1,000
  • schema-partition-lifecycle - Use partitioning for data lifecycle, not queries
  • schema-partition-query-tradeoffs - Understand partition pruning trade-offs
  • schema-partition-start-without - Consider starting without partitioning

Schema Design - JSON (MEDIUM)

  • schema-json-when-to-use - JSON for dynamic schemas; typed columns for known

Query Optimization - JOINs (CRITICAL)

  • query-join-choose-algorithm - Select algorithm based on table sizes
  • query-join-use-any - ANY JOIN when only one match needed
  • query-join-filter-before - Filter tables before joining
  • query-join-consider-alternatives - Dictionaries/denormalization vs JOIN
  • query-join-null-handling - join_use_nulls=0 for default values

Query Optimization - Indices (HIGH)

  • query-index-skipping-indices - Skipping indices for non-ORDER BY filters

Query Optimization - Materialized Views (HIGH)

  • query-mv-incremental - Incremental MVs for real-time aggregations
  • query-mv-refreshable - Refreshable MVs for complex joins

Insert Strategy - Batching (CRITICAL)

  • insert-batch-size - Batch 10K-100K rows per INSERT

Insert Strategy - Async (HIGH)

  • insert-async-small-batches - Async inserts for high-frequency small batches
  • insert-format-native - Native format for best performance

Insert Strategy - Mutations (CRITICAL)

  • insert-mutation-avoid-update - ReplacingMergeTree instead of ALTER UPDATE
  • insert-mutation-avoid-delete - Lightweight DELETE or DROP PARTITION

Insert Strategy - Optimization (HIGH)

  • insert-optimize-avoid-final - Let background merges work

When to Apply

This skill activates when you encounter:

  • CREATE TABLE statements
  • ALTER TABLE modifications
  • ORDER BY or PRIMARY KEY discussions
  • Data type selection questions
  • Slow query troubleshooting
  • JOIN optimization requests
  • Data ingestion pipeline design
  • Update/delete strategy questions
  • ReplacingMergeTree or other specialized engine usage
  • Partitioning strategy decisions

Rule File Structure

Each rule file in rules/ contains:

  • YAML frontmatter: title, impact level, tags
  • Brief explanation: Why this rule matters
  • Incorrect example: Anti-pattern with explanation
  • Correct example: Best practice with explanation
  • Additional context: Trade-offs, when to apply, references

Full Compiled Document

For the complete guide with all rules expanded inline: AGENTS.md

Use AGENTS.md when you need to check multiple rules quickly without reading individual files.

how to use clickhouse-best-practices

How to use clickhouse-best-practices on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add clickhouse-best-practices
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/clickhouse/agent-skills --skill clickhouse-best-practices

The skills CLI fetches clickhouse-best-practices from GitHub repository clickhouse/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/clickhouse-best-practices

Reload or restart Cursor to activate clickhouse-best-practices. Access the skill through slash commands (e.g., /clickhouse-best-practices) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.642 reviews
  • Pratham Ware· Dec 16, 2024

    I recommend clickhouse-best-practices for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chinedu Abbas· Dec 8, 2024

    I recommend clickhouse-best-practices for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • William Dixit· Dec 4, 2024

    Useful defaults in clickhouse-best-practices — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Jin Torres· Dec 4, 2024

    Keeps context tight: clickhouse-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sophia Perez· Nov 23, 2024

    I recommend clickhouse-best-practices for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sophia Agarwal· Nov 19, 2024

    Keeps context tight: clickhouse-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sakshi Patil· Nov 7, 2024

    Useful defaults in clickhouse-best-practices — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Oct 26, 2024

    clickhouse-best-practices has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Hana Kapoor· Oct 14, 2024

    clickhouse-best-practices reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Luis Mensah· Oct 10, 2024

    Registry listing for clickhouse-best-practices matched our evaluation — installs cleanly and behaves as described in the markdown.

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