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Found 33 Skills
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.
Diagnose and resolve ClickHouse grant and authentication errors, especially after upgrades. Use when queries fail with ACCESS_DENIED/NOT_ENOUGH_PRIVILEGES, AUTHENTICATION_FAILED/WRONG_PASSWORD/REQUIRED_PASSWORD, or ON CLUSTER privilege errors; when system.* or INFORMATION_SCHEMA access is denied; or when grant behavior changes after version upgrades.
Diagnose ClickHouse RAM usage, OOM errors, memory pressure, and allocation patterns. Use for memory-related issues and out-of-memory errors.
Analyze ClickHouse system log table health including TTL configuration, disk usage, freshness, and cleanup. Use for system log issues and TTL configuration.
Optimizes ClickHouse queries for speed and efficiency. Helps with primary key design, sparse indexes, data skipping indexes (minmax, set, bloom filter, ngrambf_v1), partitioning strategies, projections, PREWHERE optimization, approximate functions, and query profiling with EXPLAIN. Use when writing ClickHouse queries, designing table schemas, analyzing slow queries, or implementing analytical aggregations. Works with columnar OLAP workloads.
Complete ClickHouse operations guide for DevOps and SRE teams managing production deployments. Provides practical guidance on monitoring essential metrics (query latency, throughput, memory, disk), introspecting system tables, performance analysis, scaling strategies (vertical and horizontal), backup/disaster recovery, tuning at query/server/table levels, and troubleshooting common issues. Use when diagnosing ClickHouse problems, optimizing performance, planning capacity, setting up monitoring, implementing backups, or managing production clusters. Includes resource management strategies for disk space, connections, and background operations plus production checklists.
Query Ethereum network data via ethpandaops CLI or MCP server. Use when analyzing blockchain data, block timing, attestations, validator performance, network health, or infrastructure metrics. Provides access to ClickHouse (blockchain data), Prometheus (metrics), Loki (logs), and Dora (explorer APIs).
ClickHouse migration patterns and rules. Use when creating or modifying ClickHouse migrations.
ClickHouse Cloud user and permission management. TRIGGERS - create ClickHouse user, ClickHouse permissions, ClickHouse Cloud credentials.
Generate DBeaver config from Pydantic ClickHouse models. TRIGGERS - DBeaver config, ClickHouse connection, database client config.
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.