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- 310 registry:skill items, 1528 files, 7 categories (hub tags) - registry.json generated by @kortix/registry buildRegistry - nested SKILL.md in sn-da-* renamed SUBSKILL.md (no phantom skills) - provenance + licenses: SOURCES.md
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Example: SIEM / security analytics
Scenario
- Workload: endpoint, identity, cloud, and network telemetry
- Query pattern:
- repeated detection logic
- time-bounded investigations
- lookup-heavy enrichments
- Freshness target: near real-time
Workload Summary
This workload is time-series heavy, multi-source, and often enrichment-bound. The two common failure modes are:
- expensive runtime JOINs on slow-changing dimension data
- micro-batched ingest that creates excessive parts
Key Decisions
- Use append-friendly event storage
- Replace repeated small-dimension JOINs where possible
- Protect hot detections with precomputation or lookup structures
Recommendations
1. Enable async inserts for small-batch telemetry senders
What
Use async inserts when many agents or producers write very small batches.
Why
This reduces small-part pressure without rewriting every upstream sender.
Category
official
Confidence
high
Source
- https://clickhouse.com/docs/en/operations/settings/settings#async_insert
- https://clickhouse.com/docs/optimize/asynchronous-inserts
2. Use dictionaries for slow-changing asset and identity lookups
What
Move repeated device-owner or asset-lookup enrichment out of runtime joins where appropriate.
Why
Security detections often execute continuously; repeated joins on slow-changing dimensions waste CPU.
Category
official
Confidence
high
Source
3. Use incremental MVs for repeated aggregated detection views
What
Precompute common counts, rates, and rollups that power dashboards or recurring detections.
Why
Not every threat-hunting query should hit the same raw telemetry tables repeatedly.
Category
official
Confidence
high
Source