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clickhouse-architecture-advisor

clickhouse/agent-skills
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Design ClickHouse architectures and choose ingestion and data modeling patterns for specific workloads.
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.

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The full skill.

Original instructions from the publisher’s SKILL.md

# 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:

```markdown
## 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.