cc-skill-clickhouse-io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
58
37%
Does it follow best practices?
Impact
97%
1.27xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/cc-skill-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 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
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides excellent, executable code examples across many ClickHouse use cases, making it highly actionable. However, it suffers from significant verbosity — explaining concepts Claude already knows, including a generic overview section, and packing everything into a single monolithic file without any progressive disclosure. The lack of validation steps in workflows involving data pipelines and the absence of error handling patterns are notable gaps.
Suggestions
Remove the overview section explaining what ClickHouse is and its key features — Claude already knows this. Cut the 'When to Use' placeholder entirely.
Split content into separate files: keep SKILL.md as a concise overview with links to files like QUERY_PATTERNS.md, TABLE_DESIGN.md, DATA_PIPELINES.md, and MONITORING.md.
Add validation checkpoints to the ETL and CDC patterns — e.g., verify row counts after bulk insert, handle insertion errors, and include retry logic.
Remove explanatory comments like '-- For data that may have duplicates' and 'Don't do this in a loop!' that explain obvious concepts, keeping only the ✅/❌ markers for good/bad 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 document is ~350 lines with significant verbosity, including explanatory comments and a generic 'Best Practices' section with advice Claude would already understand. The '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, and monitoring queries are all copy-paste ready with concrete column names and realistic patterns. | 3 / 3 |
Workflow Clarity | The ETL pipeline has a clear 3-step sequence, and the document covers table design → queries → insertion → materialized views in a logical order. However, there are no validation checkpoints — no steps to verify table creation succeeded, no error handling in the TypeScript code, and no feedback loops for data pipeline failures. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content (~350 lines) with no references to external files. Table design, query optimization, insertion patterns, materialized views, monitoring, analytics queries, pipeline patterns, and best practices are all inlined. Much of this content (e.g., common analytics queries, CDC patterns) should be split into separate reference files. | 1 / 3 |
Total | 7 / 12 Passed |
Validation
90%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
- sickn33/antigravity-awesome-skills
- Commit
e18e63c
- Reviewed
Table of Contents
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