BB huawei-cloud-mrs-clickhouse-sql-check
Comprehensive SQL statement checking for ClickHouse, supporting multiple kernel versions (24.8, 23.3, 22.3) and two check modes: 1. Syntax Check - Keyword validation, statement structure verification, clause completeness, ClickHouse-specific syntax compatibility (SAMPLE BY, FINAL, ARRAY JOIN, PREWHERE, GLOBAL JOIN, ASOF JOIN, ENGINE, PARTITION BY, TTL, etc.) based on kernel source grammar 2. Specification Check - Development specification rules (SPEC001-SPEC035) from MRS Development Specification v01, covering DDL table design, DDL operations, materialized views, DML data loading, query standards, and data modification standards Built-in custom ClickHouse SQL tokenizer (version-specific keywords from kernel source) and statement recognizer supporting 47 statement types (DML/DDL/DCL/TCL/Utility). Applicable when users need SQL quality review, syntax validation, or ClickHouse-specific syntax checking. Trigger: "Clickhouse SQL check"、"CK SQL check"、 "Clickhouse SQL 校验"、 "Clickhouse SQL 检查"、 "Clickhouse SQL specification", "Clickhouse SQL 规范检查"、"Clickhouse SQL optimization", "Clickhouse SQL 优化"、"检查 Clickhouse SQL"
Comprehensive SQL statement checking for ClickHouse, supporting multiple kernel versions (24.8, 23.3, 22.3) and two check modes: 1. Syntax Check - Keyword…
As a process B 68/100 · Nearly there — weak spots: when it triggers, progress reporting
How to improve
- Shorten the description to 1024 characters.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1140 chars, limit 1024 - warning
body-longSKILL.md body ≈ 5302 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 68/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5302 tokens
- 85Steps. 56 steps, 3 vague phrases
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- low 10 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1139: 120–800 characters recommended
- -212 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 9 example trigger phrases
- +4Structure: 31 headings
- +3Step-by-step instructions: 56 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.