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Found 54 Skills
Diagnose ClickHouse INSERT performance, batch sizing, part creation patterns, and ingestion bottlenecks. Use for slow inserts and data pipeline issues.
Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically — including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like "my inserts keep failing" or "connection drops randomly" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.
Diagnose ClickHouse merge performance, part backlog, and 'too many parts' errors. Use for merge issues and part management problems.
Analyze whether ClickHouse indexes (PRIMARY KEY, ORDER BY, skipping indexes, projections) are being used effectively for actual query patterns. Use when investigating index effectiveness, ORDER BY key design, query-to-index alignment, or when queries scan more data than expected.
Generate DBeaver config from Pydantic ClickHouse models. TRIGGERS - DBeaver config, ClickHouse connection, database client config.
Track and diagnose ClickHouse ALTER UPDATE, ALTER DELETE, and other mutation operations. Use for stuck mutations and mutation performance issues.
Diagnose ClickHouse issues by analyzing system.part_log (part creation, merges, mutations, downloads, removals, moves). Use for too many parts / micro-batch inserts, merge backlog or slow merges, mutation storms (ALTER DELETE/UPDATE), unusual replication DownloadPart churn, unexpected RemovePart spikes, or ZooKeeper/Keeper znode growth correlated with part activity.
Diagnose ClickHouse SELECT query performance, analyze query patterns, identify slow queries, and find optimization opportunities. Use for query latency and timeout issues.
Analyze ClickHouse cache systems including mark cache, uncompressed cache, and query cache. Use for cache hit ratio issues and cache tuning.
Writing or debugging tests, choosing unit vs integration style, Postgres/ClickHouse tests, regenerating ClickHouse test schema, or exporting test helpers from packages without pulling test code into production bundles.
Patterns for efficient ML data pipelines using Polars, Arrow, and ClickHouse. TRIGGERS - data pipeline, polars vs pandas, arrow format, clickhouse ml, efficient loading, zero-copy, memory optimization.
Analyze ClickHouse table structure, partitioning, ORDER BY keys, materialized views, and identify schema design anti-patterns. Use for table design issues and optimization.