clickhouse-io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Overall
score
58%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
33%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 selection criteria, and the absence of a 'Use when...' clause significantly limits Claude's ability to know when to select this skill over other database-related skills.
Suggestions
Add a 'Use when...' clause with explicit triggers like 'Use when working with ClickHouse databases, columnar storage, OLAP queries, or when the user mentions ClickHouse, MergeTree engines, or analytical data warehousing.'
Replace broad categories with specific concrete actions such as 'Write efficient aggregation queries, configure MergeTree table engines, optimize materialized views, design partition strategies.'
Include common user terminology variations like 'OLAP', 'columnar database', 'CH', 'analytical warehouse', or 'time-series analytics' to improve trigger term coverage.
| 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
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides comprehensive, actionable ClickHouse patterns with excellent executable code examples covering table design, queries, and data pipelines. However, it's verbose with unnecessary introductory content, lacks validation checkpoints in workflows, and would benefit from being split into focused sub-documents for better progressive disclosure.
Suggestions
Remove the Overview section entirely - Claude knows what ClickHouse is and its key features
Add validation steps to the ETL and CDC patterns (e.g., row count verification, error handling, retry logic)
Split into separate files: TABLE_ENGINES.md, QUERY_PATTERNS.md, DATA_PIPELINES.md, with SKILL.md as a brief overview linking to each
Add explicit error handling examples for the TypeScript insertion patterns showing what to do when inserts fail
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The overview section explains what ClickHouse is and lists basic features Claude already knows. The content is generally useful but includes unnecessary explanations like 'ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP)' and feature lists that add little value. | 2 / 3 |
Actionability | Excellent executable code examples throughout - SQL table definitions, TypeScript insertion patterns, monitoring queries, and analytics queries are all copy-paste ready with realistic field names and proper syntax. | 3 / 3 |
Workflow Clarity | The ETL pattern shows a clear sequence, but most sections present patterns without validation checkpoints. The bulk insert vs individual insert comparison is helpful, but there's no guidance on verifying data was inserted correctly or handling failures in the pipeline patterns. | 2 / 3 |
Progressive Disclosure | Content is well-organized with clear section headers, but it's a monolithic 300+ line file that could benefit from splitting into separate files (e.g., TABLE_DESIGN.md, QUERY_PATTERNS.md, PIPELINES.md). No references to external documentation or supplementary files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
63%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 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
license_field | 'license' field is missing | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_output_format | No obvious output/return/format terms detected; consider specifying expected outputs | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 10 / 16 Passed | |
- Reviewed
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