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 ComeOnOliver/skillshub --skill clickhouse-iogit clone --depth 1 https://github.com/ComeOnOliver/skillshubWrote 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/comeonoliver/skillshub/clickhouse-io)<a href="https://agentmods.dev/skills/comeonoliver/skillshub/clickhouse-io"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/clickhouse-io/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/comeonoliver/skillshub/clickhouse-io"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/clickhouse-io.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 | $0.00026 | $0.02458 |
| Opus 5 | $0.00013 | $0.01229 |
| Sonnet 5 | $0.00005 | $0.00492 |
| Haiku 4.5 | $0.00003 | $0.00246 |
Grade A, and why
clickhouse-io 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.
This is a copy
80% identical to clickhouse-io — 132 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ClickHouse Analytics Patterns
ClickHouse-specific patterns for high-performance analytics and data engineering.
Overview
ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP). It's optimized for fast analytical queries on large datasets.
Key Features:
- Column-oriented storage
- Data compression
- Parallel query execution
- Distributed queries
- Real-time analytics
Table Design Patterns
MergeTree Engine (Most Common)
CREATE TABLE markets_analytics (
date Date,
market_id String,
market_name String,
volume UInt64,
trades UInt32,
unique_traders UInt32,
avg_trade_size Float64,
created_at DateTime
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (date, market_id)
SETTINGS index_granularity = 8192;
ReplacingMergeTree (Deduplication)
-- For data that may have duplicates (e.g., from multiple sources)
CREATE TABLE user_events (
event_id String,
user_id String,
event_type String,
timestamp DateTime,
properties String
) ENGINE = ReplacingMergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (user_id, event_id, timestamp)
PRIMARY KEY (user_id, event_id);
AggregatingMergeTree (Pre-aggregation)
-- For maintaining aggregated metrics
CREATE TABLE market_stats_hourly (
hour DateTime,
market_id String,
total_volume AggregateFunction(sum, UInt64),
total_trades AggregateFunction(count, UInt32),
unique_users AggregateFunction(uniq, String)
) ENGINE = AggregatingMergeTree()
PARTITION BY toYYYYMM(hour)
ORDER BY (hour, market_id);
-- Query aggregated data
SELECT
hour,
market_id,
sumMerge(total_volume) AS volume,
countMerge(total_trades) AS trades,
uniqMerge(unique_users) AS users
FROM market_stats_hourly
WHERE hour >= toStartOfHour(now() - INTERVAL 24 HOUR)
GROUP BY hour, market_id
ORDER BY hour DESC;
Query Optimization Patterns
Efficient Filtering
-- ✅ GOOD: Use indexed columns first
SELECT *
FROM markets_analytics
WHERE date >= '2025-01-01'
AND market_id = 'market-123'
AND volume > 1000
ORDER BY date DESC
LIMIT 100;
-- ❌ BAD: Filter on non-indexed columns first
SELECT *
FROM markets_analytics
WHERE volume > 1000
AND market_name LIKE '%election%'
AND date >= '2025-01-01';
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 · 431 lines · 26 tokens per session scan A 500ff44e9e60
clickhouse-io is a skill published in the GitHub repository ComeOnOliver/skillshub (63 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 2,458 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to clickhouse-io, differing in 132 lines, and is treated as a copy.
Other skills, from other repositories
cmdb-patterns
Create ServiceNow CIs and cmdbrelci relationships, walk upstream/downstream impact, detect orphan/stale CIs, and align discovered CIs with the proper sysclassname hierarchy.
data-policies
Manage ServiceNow dictionary (sysdictionary), table/field creation, choice lists, dictionary overrides, and sysdatapolicy2 rules that enforce mandatory/read-only/visible field behavior on the data layer.
import-export
Move data in and out of ServiceNow — CSV parsing into import sets, GlideImportSetTransformer runs, CSV/JSON/XML exports, scheduled data sources, bulk update and deleteMultiple safety patterns.
gliderecord-patterns
Write efficient ServiceNow GlideRecord queries — addQuery vs addEncodedQuery, setLimit, GlideAggregate for counts, avoiding N+1 in loops, query operators, and safe CRUD with workflow control.
misata
Generate realistic multi-table test data, seed a development database, or build fixtures whose joins and totals actually hold. Use when the user needs test data, sample data, demo data, seed data, fixtures, a populated dev/staging database, or a relational dataset shaped to specific numbers (a revenue curve, a churn…
status
Verify gnosis-mcp server connectivity, schema integrity, and corpus health. Use when MCP calls fail, return empty, or return unexpected data.