A workload-first decision aid for ClickHouse ingestion, enrichment, time-series storage and changing data.
Editorial review checked 2026-10-29 against this instruction snapshot. Supporting scripts and runtime behavior remain outside this editorial review.
This source asks the agent to establish the workload and latency target before selecting an architecture. It routes individual decisions to rule files and distinguishes documented behavior, deductions and experience-based heuristics. That provenance separation helps a reviewer understand why a recommendation was made.
Teams choosing ingestion and aggregation patterns, especially when a generic database recommendation does not explain the workload tradeoffs.
The entry file is a router, not a complete architecture reference or capacity model. Its linked rules and current official documentation still need inspection. A heuristic that worked for telemetry may fail for mutable business records or point lookups.
Unknown; inspect source requirements and supporting scripts.
Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code; this catalog never runs it.
npx skills add https://github.com/ClickHouse/agent-skills/tree/69a8354b05b5942e5cea77b6c87be7788303fc18/skills/clickhouse-architecture-advisor --skill clickhouse-architecture-advisorReview pinned skill folder ↗ · CLI documentation ↗
After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.
Static review only. This is not a security audit or a guarantee of safety.
SKILL.md text only; scripts, dependencies and runtime behavior not audited.
No configured text patterns matched. Unmatched or concealed behavior can still exist.
Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.
86/100 · Metadata coverage 100% · Model 1.0
Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.
Write down arrival rate, query shapes, retention and correctness requirements. Compare two plausible designs and attach a source and uncertainty level to each material decision before proposing schema changes.
Use representative data to measure ingest lag, query latency and correction handling. Check late events and changed records explicitly, and keep measured results separate from recommendations that remain assumptions.
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A Python DataFrame workflow for exploring files and joining tabular sources through the embedded ClickHouse engine.
Embedded analytical SQL for Python tasks that need ClickHouse functions without operating a separate database server.
A structured review of ClickHouse schema, query and insertion choices with links to focused rules.