jbvc/clickhouse-io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
45
45%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
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 which provides some distinctiveness, but it reads more like a topic list than an actionable skill description. It lacks specific concrete actions, natural trigger term variations, and critically missing a 'Use when...' clause to guide skill selection.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about ClickHouse tables, queries, schema design, or mentions ClickHouse, columnar databases, or OLAP workloads.'
List specific concrete actions such as 'Design MergeTree table schemas, optimize ClickHouse queries, create materialized views, configure partitioning and indexing strategies.'
Include natural trigger term variations users might say: 'ClickHouse', 'CH', 'columnar database', 'OLAP', 'MergeTree', 'ReplacingMergeTree', '.clickhouse', 'analytical database'.
| 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 weak, so this scores 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 the broad terms 'analytics', 'data engineering best practices', and 'query optimization' could overlap with general SQL, database, or data engineering skills. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
29%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides extensive, executable code examples covering ClickHouse table design, queries, and data pipelines, which is its primary strength. However, it is significantly too long and monolithic, explaining concepts Claude already knows (what ClickHouse is, what column-oriented storage means), and lacks any workflow sequencing, validation steps, or progressive disclosure through external file references. The TypeScript insertion example also has a SQL injection vulnerability that goes unaddressed.
Suggestions
Remove the Overview section entirely (Claude knows what ClickHouse is) and trim the 'When to Activate' list to save ~20 lines of unnecessary context.
Split into multiple files: keep SKILL.md as a concise overview with links to separate files like SCHEMA_PATTERNS.md, QUERY_COOKBOOK.md, DATA_PIPELINES.md, and MONITORING.md.
Add explicit workflow sequences with validation for multi-step operations like schema migrations, ETL pipelines, and materialized view creation (e.g., validate schema -> test with sample data -> deploy).
Fix the SQL injection vulnerability in the bulk insert TypeScript example by using parameterized queries or the ClickHouse client's native insert API, and add error handling patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The overview section explains what ClickHouse is and lists basic features (column-oriented storage, parallel execution, etc.) that Claude already knows. The 'When to Activate' section is verbose. The document is ~350 lines with significant redundancy (e.g., querying materialized views shown twice with near-identical SQL). Many patterns are generic analytics queries (retention, funnel, cohort) that don't add ClickHouse-specific value beyond standard SQL. | 1 / 3 |
Actionability | The skill provides fully executable SQL and TypeScript code throughout — CREATE TABLE statements, INSERT patterns, monitoring queries, and ETL examples are all copy-paste ready with concrete column names, types, and realistic data patterns. | 3 / 3 |
Workflow Clarity | There are no sequenced multi-step workflows with validation checkpoints. The ETL pattern is a loose code sketch without error handling or validation. The bulk insert TypeScript example has SQL injection vulnerabilities with no mention of parameterized queries or validation. No feedback loops for schema changes, migrations, or data pipeline failures. | 1 / 3 |
Progressive Disclosure | This is a monolithic wall of content (~350 lines) with no references to external files. Content like the full analytics query cookbook (retention, funnel, cohort analysis), data pipeline patterns, and performance monitoring could easily be split into separate reference files. Everything is inline with no navigation structure. | 1 / 3 |
Total | 6 / 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.
Reviewed
Table of Contents