🎯Skills11
Official ClickHouse best practices skill with 28 rules across schema design, query optimization, and data ingestion, prioritized by impact
Provides 5 decision frameworks for workload-aware ClickHouse architecture decisions, covering ingestion strategy, join patterns, late-arriving data, time-series partitioning, and real-time pre-aggregation for production deployments.
Official ClickHouse agent skill providing AI coding assistants with expertise in chdb SQL queries for in-process analytics with Python, covering schema design, query optimization, and data ingestion best practices.
Official ClickHouse agent skill providing 28 best-practice rules for schema design, query optimization, and data ingestion, supporting local development with clickhousectl CLI including primary key selection, JOIN optimization, and partitioning strategies.
Official ClickHouse agent skill providing AI coding assistants with chdb datastore capabilities for persistent in-process data storage, covering data management patterns with ClickHouse's embedded analytics engine.
An official ClickHouse agent skill that teaches AI agents best practices for ClickHouse Cloud deployment, covering schema design, query optimization, and data ingestion patterns across 28 prioritized rules.
Official ClickHouse agent skills providing 28 best-practice rules for schema design, query optimization, and data ingestion, plus chdb (in-process ClickHouse for Python) expertise. Covers primary key selection, JOIN optimization, partitioning strategy, materialized views, and troubleshooting patterns.
The official ClickHouse agent skills repository, providing AI coding agents with domain-specific expertise in ClickHouse and chdb (in-process ClickHouse for Python). Includes 28 prioritized rules covering schema design, query optimization, JOIN strategies, data ingestion patterns, and in-process analytics.
Official ClickHouse agent skills providing 28 best-practice rules for schema design, query optimization, and data ingestion, covering both open-source ClickHouse and ClickHouse Cloud. Also includes skills for chdb, the in-process ClickHouse engine for Python.
Skill
Skill
🔌Plugins8
Local and cloud workflows for running ClickHouse with clickhousectl — local development (install ClickHouse, start a server, create schemas, seed data) and ClickHouse Cloud deployment (authenticate, create a service, migrate schemas, connect an application), with a decision tree routing between them
28 best practice rules for ClickHouse schema design, query optimization, and data ingestion — prioritized by impact
Workload-aware architecture decision skill for ClickHouse — ingestion strategies, partitioning, enrichment, upsert patterns, and pre-aggregation with explicit official/derived/field provenance
Embedded ClickHouse SQL engine for Python via chDB — run analytical SQL on parquet/csv/json files, URLs, S3, and remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) with Session for stateful pipelines, parametrized queries, UDFs, and cross-source joins
Pandas-compatible DataStore API for chDB — same pandas API backed by ClickHouse for 10-100x faster filter/group/aggregate/join, with built-in readers for S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, and Delta Lake and cross-source DataFrame joins
Step-by-step workflow for wiring an OpenTelemetry collector into a Managed ClickStack service on ClickHouse Cloud — deploying a new local collector or configuring an existing one, creating a dedicated ingest user, sending synthetic telemetry, and verifying it in ClickStack
Troubleshooting guide for the ClickHouse Node.js client (@clickhouse/client) — socket hang-up/ECONNRESET, Keep-Alive, data type mismatches, read-only users, proxy/pathname URLs, TLS, compression, logging, and query parameters
Local and cloud workflows for running Postgres with clickhousectl — local Docker-backed Postgres for development (start, psql client, .env wiring, lifecycle) and managed ClickHouse Cloud Postgres services, beta (authentication, service creation, connections and TLS, runtime config, read replicas, failover, point-in-time restore), with a decision tree routing between them