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/clickhouse-architecture-advisor

@5e162d6 official
by clickhouseclickhouse/agent-skills543 stars
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MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.

Use this Skill: https://skilld.dev/gh/clickhouse/agent-skills/clickhouse-architecture-advisor

This session only. Nothing lands on disk.

SKILL.md

≈74 tokens always: the name and description. ≈692 when used: this file. ≈6.4k more on demand in 14 files.

ClickHouse Architecture Advisor

This skill adds workload-aware architecture decisioning on top of clickhouse-best-practices.

Official docs remain the source of truth. This skill must always prefer official ClickHouse documentation when available.

Required behavior

Before producing recommendations:

  1. Identify the workload shape
    • observability
    • security / SIEM
    • product analytics
    • IoT / telemetry
    • market data / financial services
    • mixed OLAP with point-lookups
  2. Read the relevant decision rule files in rules/
  3. Use mappings/doc_links.yaml to attach official documentation
  4. Classify every recommendation as:
    • official
    • derived
    • field
  5. Never present field guidance as official guidance
  6. If a recommendation is uncertain, say so explicitly

Provenance rules

official

Use this when the recommendation is directly backed by official docs.

derived

Use this when the recommendation is not stated verbatim in docs but follows logically from documented ClickHouse behavior.

field

Use this only for experience-based guidance that may be situational. When using field, include:

  • a disclaimer that the advice is heuristic
  • a relevant official doc if one partially applies
  • the reason the advice depends on workload context

Read these rule files by scenario

Real-time ingestion design

  1. rules/decision-ingestion-strategy.md
  2. rules/decision-real-time-preaggregation.md
  3. Relevant best-practices insert rules

Time-series and retention design

  1. rules/decision-partitioning-timeseries.md
  2. Relevant best-practices schema partition rules

Enrichment and dimension lookups

  1. rules/decision-join-enrichment.md
  2. Relevant best-practices query join rules

Mutable state / late-arriving events

  1. rules/decision-late-arriving-upserts.md
  2. Relevant best-practices mutation avoidance rules

Output format

Structure responses like this:

## Workload Summary
- workload:
- latency target:
- data shape:
- primary query patterns:
- operational constraints:

## Key Decisions
- ...
- ...

## Recommendations

### <Recommendation title>

**What**
...

**Why**
...

**How**
...

**Category**
official | derived | field

**Confidence**
high | medium | heuristic

**Source**
- doc link(s)

**Validation**
- concrete SQL, metric, or smoke test

Architecture-specific guidance

Prefer decision frameworks over generic advice. Good responses should:

  • explain tradeoffs
  • identify the likely operating bottleneck
  • separate immediate actions from structural redesign
  • provide target architecture patterns, not just isolated settings

Full reference

See AGENTS.md for the compiled version and examples/ for sample outputs.

Source: SKILL.md on GitHub

No alerts17d4 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    The skill is a safe architectural advisor for ClickHouse workloads. It provides structured decision frameworks for ingestion, partitioning, and schema design based on official documentation. No malicious patterns, data exfiltration, or dangerous execution triggers were detected.

  • Socket17d

    No alerts

  • Snyk17d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 5e162d6. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

Activeupdated 6 months ago
metadata
{
  "author": "ClickHouse Inc",
  "version": "0.1.0"
}
  • clickhouse
  • architecture
  • olap
  • ingestion
  • time-series
  • schema-design
  • partitioning
  • joins
  • telemetry

README badge

README badge for clickhouse/agent-skills/clickhouse-architecture-advisor

Guides ClickHouse architecture decisions for specific workloads—observability, analytics, IoT, financial services—by mapping workload shape to ingestion, partitioning, and join strategies with official documentation links. Classifies recommendations by provenance (official, derived, field) to separate documented behavior from heuristic field guidance.

Generated from the current SKILL.md.

Does this skill replace the clickhouse-best-practices skill?
No. This skill complements clickhouse-best-practices by adding workload-aware decision frameworks and provenance labels. Official documentation remains the source of truth for both.
What workload types does this skill cover?
Observability, security/SIEM, product analytics, IoT/telemetry, market data/financial services, and mixed OLAP with point-lookups. Each has scenario-specific rule files for ingestion, time-series retention, enrichment, and late-arriving events.
How does this skill distinguish between official, derived, and field guidance?
Official recommendations are directly from ClickHouse docs. Derived recommendations follow logically from documented behavior. Field recommendations are experience-based and include a disclaimer that they are heuristic and workload-dependent.
What should I do if a recommendation is uncertain?
The skill explicitly states when a recommendation is uncertain rather than presenting it as confident guidance.

Generated from the current SKILL.md. These answers refresh after source changes.