Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vaquarkhan/data-engineering-agent-skills --skill clickhouse-real-time-analyticsgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/clickhouse-real-time-analytics)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/clickhouse-real-time-analytics"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/clickhouse-real-time-analytics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/clickhouse-real-time-analytics"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/clickhouse-real-time-analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00991 |
| Opus 5 | $0.00022 | $0.00495 |
| Sonnet 5 | $0.00009 | $0.00198 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
clickhouse-real-time-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ClickHouse Real Time Analytics
Overview
Use this skill when ClickHouse is the target for low-latency analytical serving. It helps agents design ingestion, partitioning, materialized views, and query-ready schemas for fast reads while maintaining operational safety and cost control.
When to Use
- designing or modifying
ClickHousetables for real-time analytics - building event-heavy analytical aggregation layers
- creating materialized views for pre-computed metrics
- optimizing low-latency dashboards and metric APIs
- planning ingestion patterns for high-throughput event streams
Do not use this when the workload is better served by a transactional database or a batch-oriented warehouse with no latency requirement.
Workflow
-
Define latency, freshness, and query access patterns. Include:
- acceptable query latency targets (p50, p99)
- data freshness requirements (seconds, minutes, eventual)
- primary query patterns (point lookups, time-range scans, aggregations)
- expected concurrent query load and user base
-
Choose the right table engine and schema design.
MergeTreefamily for most analytical workloadsReplacingMergeTreefor deduplication on eventual consistencyAggregatingMergeTreefor pre-aggregated rollupsCollapsingMergeTreeorVersionedCollapsingMergeTreefor mutable state- define sort keys aligned with primary query filters
- choose partition keys for lifecycle management, not query speed
-
Design ingestion for throughput and merge safety.
- batch inserts over single-row writes (target 1000+ rows per insert)
- avoid too many partitions — high partition counts cause merge pressure
- use
Buffertables or async insert when write concurrency is high - define deduplication strategy if at-least-once delivery is the source guarantee
-
Build materialized views with explicit contracts.
- materialized views are insert-triggered, not retroactive
- define what happens when the source schema changes
- document the lag between source insert and view availability
- test that view aggregations remain correct after merges
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 92 lines · 44 tokens per session scan A ee91c7005248
clickhouse-real-time-analytics is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (44 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 991 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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