ClickHouse/agent-skills is a collection of packaged instructions that give AI coding agents specialized guidance for ClickHouse databases and chdb, an in-process version of ClickHouse for Python. It helps agents design schemas, write queries, ingest data, analyze results, and troubleshoot performance with ClickHouse or ClickHouse Cloud. The catalogue entries are the repository’s skills, plugins, and instruction for using these systems.
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 ClickHouse/agent-skills --skill clickhouse-architecture-advisorgit clone --depth 1 https://github.com/ClickHouse/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/clickhouse/agent-skills/clickhouse-architecture-advisor)<a href="https://agentmods.dev/skills/clickhouse/agent-skills/clickhouse-architecture-advisor"><img src="https://agentmods.dev/badge/skills/clickhouse/agent-skills/clickhouse-architecture-advisor/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/clickhouse/agent-skills/clickhouse-architecture-advisor"><img src="https://agentmods.dev/badge/skills/clickhouse/agent-skills/clickhouse-architecture-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00049 | $0.00680 |
| Opus 5 | $0.00024 | $0.00340 |
| Sonnet 5 | $0.00010 | $0.00136 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
clickhouse-architecture-advisor 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Identify the workload shape
- observability
- security / SIEM
- product analytics
- IoT / telemetry
- market data / financial services
- mixed OLAP with point-lookups
- Read the relevant decision rule files in
rules/ - Use
mappings/doc_links.yamlto attach official documentation - Classify every recommendation as:
officialderivedfield
- Never present field guidance as official guidance
- 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
rules/decision-ingestion-strategy.mdrules/decision-real-time-preaggregation.md- Relevant best-practices insert rules
Time-series and retention design
rules/decision-partitioning-timeseries.md- Relevant best-practices schema partition rules
Enrichment and dimension lookups
rules/decision-join-enrichment.md- Relevant best-practices query join rules
Mutable state / late-arriving events
rules/decision-late-arriving-upserts.md- Relevant best-practices mutation avoidance rules
Output format
Structure responses like this:
## 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
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- AGENTS.md 2.2 KB
- examples/finserv-market-surveillance.md 3.1 KB
- examples/observability-high-throughput.md 2.6 KB
- examples/README.md 500 B
- examples/siem-security-analytics.md 1.9 KB
- mappings/doc_links.yaml 1.1 KB
- metadata.json 919 B
- README.md 2.0 KB
- rules/decision-ingestion-strategy.md 2.2 KB
- rules/decision-join-enrichment.md 2.0 KB
- rules/decision-late-arriving-upserts.md 2.0 KB
- rules/decision-partitioning-timeseries.md 2.0 KB
- rules/decision-real-time-preaggregation.md 2.0 KB
- schemas/recommendation_schema.yaml 452 B
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 · 124 lines · 49 tokens per session scan A edaba60acfe9
clickhouse-architecture-advisor is a skill published in the GitHub repository ClickHouse/agent-skills (532 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 680 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.
Other skills, from other repositories
use-gfs-mcp
GFS MCP Server for AI agent integration. Provides Model Context Protocol tools for database version control with automatic schema versioning.
use-gfs-cli
Git-like version control for databases using the GFS CLI. Manage database states with commits, branches, time travel, and schema versioning.
bigquery-graph
Skill for Graph Query Language (GQL) or SQL/PGQ queries against a property graph. Includes path finding, multi-hop traversal, topological connection, shortest path, node reachability, edge connectivity, and semantic graph queries.
cqrs-implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.
projection-patterns
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.
dbx
DBX CLI for database schema exploration and read-only queries. When the user needs to list connections, explore tables, describe schemas, run queries, or generate AI-friendly schema context from DBX-managed databases. Do NOT use for write operations unless the user explicitly confirms with --allow-writes.