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 selvarajmurugesan90/ops-engineering-skills --skill clickhouse-analytical-database-operationsgit clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-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/selvarajmurugesan90/ops-engineering-skills/clickhouse-analytical-database-operations)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/clickhouse-analytical-database-operations"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/clickhouse-analytical-database-operations/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/selvarajmurugesan90/ops-engineering-skills/clickhouse-analytical-database-operations"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/clickhouse-analytical-database-operations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00132 | $0.04017 |
| Opus 5 | $0.00066 | $0.02008 |
| Sonnet 5 | $0.00026 | $0.00803 |
| Haiku 4.5 | $0.00013 | $0.00402 |
Grade A, and why
clickhouse-analytical-database-operations 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 11d 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 — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ClickHouse Analytical Database Operations
Purpose
ClickHouse is a column-oriented OLAP database built for aggregating
billions of rows in sub-second time, at the cost of assumptions that
are the opposite of a typical OLTP engine: it favors large batched
inserts over row-by-row writes, has no transactional multi-row UPDATE/
DELETE in the traditional sense (mutations are async, heavyweight
background rewrites), and gets its performance almost entirely from
choosing the right member of the MergeTree engine family and a
sensible sharding/replication topology up front. This skill covers
those foundational decisions — engine selection, ReplicatedMergeTree
plus Distributed tables for a sharded/replicated cluster, and
materialized views for streaming aggregation — the operational core
that determines whether a ClickHouse deployment stays fast as data
volume grows.
When to use
- Designing a new ClickHouse table and choosing among
MergeTree,ReplacingMergeTree,SummingMergeTree,AggregatingMergeTree, orCollapsingMergeTree/VersionedCollapsingMergeTreefor the table's actual update/deduplication pattern. - Setting up a sharded and/or replicated ClickHouse cluster
(
ReplicatedMergeTreeplusDistributedtable engine) for scale-out or fault tolerance. - Query performance is poor despite the right engine choice — usually a partition/order-by key mismatch with actual query filters.
- Building a materialized view to maintain a pre-aggregated rollup incrementally as data is inserted, instead of aggregating raw high-cardinality data on every query.
- Diagnosing excessive background merge activity, "too many parts"
errors, or a mutation (
ALTER TABLE ... UPDATE/DELETE) that's taking much longer than expected.
Prerequisites & environment
- ClickHouse 23.x/24.x assumed for the syntax below; note that
ReplicatedMergeTreehistorically required ZooKeeper for replica coordination, while ClickHouse Keeper (clickhouse-keeper, a ClickHouse-native reimplementation of the ZooKeeper protocol) is now the recommended coordination layer for new deployments — check which your cluster uses before assuming ZooKeeper-specific tooling applies. - A coordination service (ClickHouse Keeper or ZooKeeper, an odd number
of nodes — 3 minimum) reachable by every node, required for any
Replicated*table engine to coordinate replica state and part assignment. - Cluster topology defined in
config.xml/cluster.xml(<remote_servers>) soDistributedtables know the shard/replica layout — this is infrastructure configuration, not something set per-query. - Enough disk headroom for background merges: ClickHouse periodically merges smaller data parts into larger ones, and a merge briefly requires space for both the source parts and the resulting merged part simultaneously.
- Client access via the native protocol (port 9000) or HTTP interface
(port 8123);
clickhouse-clientfor interactive administration.
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.
- 11d ago First seen · 372 lines · 132 tokens per session scan A 7ec8fd7a265f
clickhouse-analytical-database-operations is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (38 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 132 tokens to every session and 4,017 once invoked, about $0.0007 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.
Other skills, from other repositories
create-pr
Creates a GitHub PR with a Linear-ticket-prefixed title and a decision-led, narrative description for prisma-next. Use when the user wants to create a pull request, open a PR, or submit changes for review.
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
supabase
Supabase / PostgREST Row-Level-Security playbook — pull the anon (or leaked servicerole) key out of the frontend JS, map tables from the auto-generated OpenAPI spec, test anonymous RLS READ disclosures (PII/secret leaks), and anonymous RLS WRITE abuse (insert/update/delete — e.g. forging…
nornicdb-cypher-queries
Pick fast, predictable Cypher query shapes in NornicDB — point lookups, batch retrieval, pagination, search, traversal, batched UNWIND/MERGE writes, cleanup, multi-tenant isolation. Use when writing or reviewing Cypher whose latency or throughput matters; maps user intent to the executor's hot-path query templates.
dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key…