clickhouse-io

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

Quality

48%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./docs/v19.7/configuration/agent/skills_external/antigravity-awesome-skills-main/skills/cc-skill-clickhouse-io/SKILL.md

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 the ClickHouse domain but relies heavily on broad category terms rather than specific capabilities. The complete absence of a 'Use when...' clause significantly weakens its utility for skill selection. While 'ClickHouse' provides some distinctiveness, the generic analytics and data engineering language creates potential overlap with other database skills.

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 actions: 'Configure MergeTree table engines, write efficient aggregation queries, optimize JOIN performance, design materialized views'

Include common user phrases and file/technology references: '.clickhouse', 'CH queries', 'columnar storage', '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, optimization, analytics) 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' which is a strong trigger term, plus 'query optimization', 'analytics', and 'data engineering'. However, missing common variations users might say like 'OLAP', 'columnar database', 'MergeTree', 'analytical queries', or file extensions.	2 / 3
Distinctiveness Conflict Risk	'ClickHouse' is distinctive, but 'query optimization', 'analytics', and 'data engineering best practices' are generic terms that could overlap with PostgreSQL, MySQL, or general database 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 excellent actionable code examples and good/bad pattern comparisons. However, it suffers from being overly long for a single skill file, includes unnecessary introductory content about what ClickHouse is, and lacks validation/verification steps for database operations that could benefit from explicit checkpoints.

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, ETL patterns to PIPELINES.md

Add validation steps to data insertion patterns (e.g., verify row counts after bulk insert, check for errors in query_log)

Add explicit error handling and rollback guidance for the CDC and ETL pipeline patterns

Dimension	Reasoning	Score
Conciseness	The overview section explains what ClickHouse is and its key features, which Claude already knows. However, the rest of the content is reasonably efficient with good code examples. Some sections like 'Best Practices' could be tighter.	2 / 3
Actionability	Provides fully executable SQL and TypeScript code examples throughout. The good/bad pattern comparisons are particularly actionable, and code is copy-paste ready with realistic table schemas and queries.	3 / 3
Workflow Clarity	The ETL pattern shows a clear sequence, but most sections present patterns without explicit validation steps. For database operations that could affect production data, there are no verification checkpoints or error recovery guidance.	2 / 3
Progressive Disclosure	Content is well-organized with clear section headers, but it's a monolithic 300+ line file. Complex topics like materialized views, CDC, and analytics queries could be split into separate reference files with the main skill providing an overview.	2 / 3
	Total	9 / 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: duclm1x1/Dive-Ai
Commit: 20ba150

Reviewed: about 2 months ago

Table of Contents

Discovery Implementation Validation

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