clickhouse-io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
59
48%
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 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 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 reference with strong actionability - the SQL and TypeScript examples are executable and well-annotated with good/bad pattern indicators. However, it's overly long for a SKILL.md overview, includes some introductory content Claude doesn't need, and lacks validation checkpoints for data operations that could benefit from explicit verification steps.
Suggestions
Remove the overview section explaining what ClickHouse is and its key features - Claude already knows this
Split into multiple files: keep quick-start patterns in SKILL.md, move detailed analytics queries to ANALYTICS.md, materialized views to MATERIALIZED_VIEWS.md, and data pipelines to PIPELINES.md
Add validation steps to the ETL and CDC patterns (e.g., row count verification, data quality checks before/after bulk inserts)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary explanations Claude already knows (e.g., 'ClickHouse is a columnar DBMS for OLAP' and listing key features). However, most content is practical code examples without excessive padding. | 2 / 3 |
Actionability | Excellent executable code examples throughout - complete SQL DDL statements, TypeScript implementations, and query patterns that are copy-paste ready. Both good and bad patterns are clearly marked with ✅ and ❌. | 3 / 3 |
Workflow Clarity | The ETL pipeline section shows a clear 3-step process, but most sections lack explicit validation checkpoints. For database operations that could affect data integrity, there are no verification steps or error recovery patterns. | 2 / 3 |
Progressive Disclosure | Content is well-organized with clear section headers, but it's a monolithic document (~400 lines) that could benefit from splitting advanced topics (materialized views, CDC, analytics queries) into separate reference files. | 2 / 3 |
Total | 9 / 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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