cc-skill-clickhouse-io

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

1.27x

Quality

31%

Does it follow best practices?

Impact

97%

1.27x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/cc-skill-clickhouse-io/SKILL.md

Quality

Content

29%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill provides a comprehensive catalog of ClickHouse patterns with strong, executable code examples across SQL and TypeScript. However, it is excessively long and monolithic, explains concepts Claude already knows (what ClickHouse is, basic features), lacks any workflow sequencing or validation checkpoints, and would benefit significantly from being split into a concise overview with references to detailed sub-files.

Suggestions

Remove the overview section explaining what ClickHouse is and its key features — Claude already knows this. Cut the 'Best Practices' section to only non-obvious, project-specific guidance.

Split the content into separate files (e.g., TABLE_DESIGN.md, QUERY_PATTERNS.md, DATA_PIPELINES.md, MONITORING.md) and make SKILL.md a concise overview with links to each.

Add validation/verification steps: after table creation (SHOW CREATE TABLE, checking partitions), after bulk inserts (SELECT count()), after materialized view creation (verify data flows correctly).

Remove the placeholder 'When to Use' section at the bottom, which adds no value.

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 document is ~350 lines with significant verbosity, including explanatory comments and a generic 'Best Practices' section with advice Claude would already know (e.g., 'Use smallest appropriate type'). The final 'When to Use' section is a meaningless placeholder.	1 / 3
Actionability	The skill provides fully executable SQL and TypeScript code examples throughout — table creation, queries, bulk inserts, materialized views, monitoring queries, and ETL patterns. The code is copy-paste ready with concrete column names, types, and realistic patterns.	3 / 3
Workflow Clarity	There is no clear multi-step workflow with sequencing or validation checkpoints. The content is organized as a reference catalog of patterns rather than a guided process. The ETL section has numbered steps but lacks validation/error handling. For a skill covering data engineering and table design, there are no verification steps (e.g., validating schema, checking insert success, testing materialized views).	1 / 3
Progressive Disclosure	The entire skill is a monolithic wall of content (~350 lines) with no references to external files. All patterns — table design, query optimization, data insertion, materialized views, monitoring, analytics queries, pipeline patterns, and best practices — are inlined. This would benefit greatly from splitting into separate reference files with a concise overview.	1 / 3
	Total	6 / 12 Passed

Description

32%Scale 1-3

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 mentions several relevant topic areas, but it reads more like a topic list than an actionable skill description. It lacks a 'Use when...' clause, specific concrete actions, and sufficient trigger term coverage to reliably distinguish it from other database or analytics skills.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about ClickHouse queries, table engines, MergeTree optimization, or columnar analytics databases.'

List specific concrete actions such as 'Designs MergeTree table schemas, optimizes ClickHouse queries, configures materialized views, and troubleshoots analytical query performance.'

Include additional natural trigger terms users might say, such as 'OLAP', 'columnar database', 'MergeTree', 'ReplacingMergeTree', 'materialized views', or 'CH'.

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 'CH'.	2 / 3
Distinctiveness Conflict Risk	'ClickHouse' is a distinct technology that provides some niche specificity, but the broader terms 'query optimization', 'analytics', and 'data engineering best practices' could easily overlap with general SQL, data warehouse, or other database skills.	2 / 3
	Total	7 / 12 Passed

Validation

90%

Warnings & errors only

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

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: skills/cc-skill-clickhouse-io/SKILL.md
Commit: fbdfd63

Reviewed: 3 days ago

Table of Contents

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