clickhouse-io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Install with Tessl CLI
npx tessl i github:ysyecust/everything-claude-code --skill clickhouse-io57
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
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 a clear technology domain (ClickHouse) but lacks explicit trigger guidance and specific concrete actions. It reads more like a topic list than actionable skill guidance, and the absence of a 'Use when...' clause significantly weakens its utility for skill selection among many options.
Suggestions
Add an explicit 'Use when...' clause with trigger terms like 'ClickHouse', 'columnar database', 'OLAP queries', 'MergeTree engine', or 'analytical workloads'.
Replace broad categories with specific concrete actions such as 'design table schemas with appropriate MergeTree engines', 'optimize aggregation queries', 'configure materialized views'.
Include common user phrasings and file/technology markers like '.clickhouse', 'CH queries', 'columnar storage', or 'real-time analytics'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (ClickHouse database) and mentions some actions like 'query optimization, analytics, and data engineering best practices' but these are broad categories rather than concrete specific actions like 'write aggregation queries' or 'configure table engines'. | 2 / 3 |
Completeness | Describes what the skill covers (ClickHouse patterns and optimization) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. | 1 / 3 |
Trigger Term Quality | Includes 'ClickHouse', 'query optimization', 'analytics', and 'data engineering' which are relevant keywords, but misses common variations users might say like 'OLAP', 'columnar database', 'MergeTree', 'analytical queries', or 'CH'. | 2 / 3 |
Distinctiveness Conflict Risk | 'ClickHouse' is a specific technology which helps distinctiveness, but 'query optimization', 'analytics', and 'data engineering best practices' are generic enough to potentially overlap with other database or analytics skills. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a comprehensive ClickHouse skill with excellent actionable code examples and clear good/bad pattern comparisons. However, it's overly verbose with unnecessary OLAP explanations, lacks validation checkpoints in multi-step workflows, and would benefit from splitting into a concise overview with linked reference files for advanced topics.
Suggestions
Remove the Overview section explaining what ClickHouse is and its key features - Claude already knows this
Add explicit validation steps to ETL and CDC workflows (e.g., 'Verify row counts match', 'Check for null values before insert')
Split into SKILL.md (quick start + common patterns) with links to separate files: ADVANCED_QUERIES.md, DATA_PIPELINES.md, PERFORMANCE.md
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary explanations Claude already knows (e.g., 'ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP)' and the Key Features list). The content is generally useful but could be tightened by removing basic OLAP/columnar concepts. | 2 / 3 |
Actionability | Excellent executable code examples throughout - SQL queries are complete and copy-paste ready, TypeScript examples are functional with proper imports, and patterns show both good and bad approaches with clear ✅/❌ markers. | 3 / 3 |
Workflow Clarity | While individual patterns are clear, multi-step processes like ETL and CDC lack explicit validation checkpoints. The ETL pipeline shows steps but doesn't include error handling or validation between extract/transform/load phases. Bulk insert patterns don't mention verifying data integrity. | 2 / 3 |
Progressive Disclosure | The content is well-organized with clear sections, but it's a monolithic document (~400 lines) that could benefit from splitting advanced topics (CDC, cohort analysis, materialized views) into separate reference files. No external file references are provided for deeper dives. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Reviewed
Table of Contents
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