clickhouse-io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
50
37%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./docs/zh-TW/skills/clickhouse-io/SKILL.mdQuality
Discovery
32%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description identifies ClickHouse as the target domain and lists broad capability areas, but lacks specific concrete actions and has no explicit 'Use when...' trigger clause. The generic terms like 'analytics' and 'data engineering best practices' could easily overlap with other database or data skills, reducing its effectiveness for skill selection.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about ClickHouse, columnar databases, OLAP queries, MergeTree engines, or analytical data pipelines.'
List specific concrete actions such as 'design MergeTree table schemas, write materialized views, optimize GROUP BY and JOIN queries, configure partitioning and sharding'.
Include natural trigger term variations users might say: 'ClickHouse', 'CH', 'columnar', 'OLAP', 'MergeTree', 'ReplacingMergeTree', '.clickhouse' to improve keyword coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (ClickHouse database) and some general action areas (query optimization, analytics, data engineering best practices), but doesn't list specific concrete actions like 'write MergeTree table definitions' or 'optimize GROUP BY queries'. | 2 / 3 |
Completeness | Describes what the skill covers at a high level but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, a missing 'Use when...' clause caps completeness at 2, and the 'what' is also fairly weak, so this scores a 1. | 1 / 3 |
Trigger Term Quality | Includes 'ClickHouse', 'query optimization', 'analytics', and 'data engineering' which are relevant keywords, but misses common user variations like 'columnar database', 'OLAP', 'MergeTree', 'materialized views', or 'analytical queries'. | 2 / 3 |
Distinctiveness Conflict Risk | 'ClickHouse' is a distinct technology that provides some niche specificity, but terms like 'query optimization', 'analytics', and 'data engineering best practices' are broad enough to overlap with general SQL or other database skills. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides comprehensive, executable ClickHouse examples covering table design, queries, and data pipelines, which is its main strength. However, it is far too verbose for a skill file—it reads more like a tutorial or documentation page, explaining concepts Claude already knows and inlining hundreds of lines that should be split into separate reference files. The lack of workflow validation steps and the monolithic structure significantly reduce its effectiveness as a skill.
Suggestions
Remove the overview section explaining what ClickHouse is and its key features—Claude already knows this. Start directly with table design patterns.
Split content into separate files: keep SKILL.md as a concise overview (~50 lines) with links to TABLE_DESIGN.md, QUERY_PATTERNS.md, ANALYTICS_QUERIES.md, and DATA_PIPELINES.md.
Add validation steps to data insertion workflows (e.g., verify row counts after bulk insert, check for errors in system.query_log).
Remove generic analytics query patterns (retention, funnel, cohort) that aren't ClickHouse-specific, or condense them to just the ClickHouse-specific syntax differences.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose (~400+ lines) and includes significant content Claude already knows: what ClickHouse is, what OLAP means, listing key features like 'columnar storage' and 'parallel query execution'. The overview section explaining ClickHouse's nature is unnecessary. Many patterns are generic SQL/analytics patterns (retention analysis, funnel analysis, cohort analysis) that don't add ClickHouse-specific value beyond basic syntax. The ETL and CDC TypeScript examples are largely boilerplate. | 1 / 3 |
Actionability | The skill provides fully executable SQL and TypeScript code examples throughout. Table creation DDL, query patterns, insert patterns, and materialized view definitions are all copy-paste ready with concrete column names, types, and realistic data scenarios. | 3 / 3 |
Workflow Clarity | While individual patterns are clear, there's no sequenced workflow with validation checkpoints. The ETL pipeline section lists steps but lacks error handling or validation. Batch insert patterns don't mention verifying data was inserted correctly. The skill is more of a reference catalog than a guided workflow, and destructive operations like data insertion lack verification steps. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content with no references to external files. All content—table design, query optimization, data insertion, materialized views, monitoring, analytics queries, data pipelines, and best practices—is inlined in a single massive document. This would benefit greatly from splitting into separate reference files (e.g., TABLE_DESIGN.md, QUERY_PATTERNS.md, ANALYTICS_QUERIES.md). | 1 / 3 |
Total | 7 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- haniakrim21/everything-claude-code
- Commit
ae2cadd
- Reviewed
Table of Contents
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