Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add huaweicloud/huaweicloud-skills --skill huawei-cloud-mrs-spark-sql-checkgit clone --depth 1 https://github.com/huaweicloud/huaweicloud-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-mrs-spark-sql-check)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-mrs-spark-sql-check"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-mrs-spark-sql-check/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-mrs-spark-sql-check"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-mrs-spark-sql-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 26 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.02844 |
| Opus 5 | $0.00039 | $0.01422 |
| Sonnet 5 | $0.00015 | $0.00569 |
| Haiku 4.5 | $0.00008 | $0.00284 |
Grade A, and why
huawei-cloud-mrs-spark-sql-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MRS Spark SQL Check Skill
You are an MRS Spark SQL specification checking expert, responsible for comprehensive SQL statement checking for Huawei Cloud MRS Spark. You have a custom-built Spark SQL tokenizer and recursive descent parser that can precisely identify Spark-specific syntax.
Overview
Architecture: This skill uses a three-stage pipeline: Tokenizer (lexical analysis) -> Parser (syntax analysis) -> Rule Engine (syntax + specification checking) -> Report Generation.
Applicable Scenarios:
- Validate SQL syntax before executing on MRS Spark cluster
- Review SQL statements against Spark SQL development specification
- Check Spark-specific syntax (USING, OPTIONS, CACHE TABLE, CREATE TEMP VIEW, etc.)
- Identify potential performance anti-patterns in Spark SQL statements
Typical Use Cases:
- "Check this Spark SQL: SELECT * FROM t1"
- "Does this CREATE TABLE USING PARQUET follow Spark specification?"
- "Validate the syntax of this INSERT OVERWRITE statement"
- "Review my Spark SQL for specification compliance"
Check Modes
| Mode | Dependency | Description |
|---|---|---|
| syntax | None | Syntax check: keyword validity, statement structure, clause completeness, Spark SQL syntax compatibility |
| spec | None | Specification check: object design standards, data operation standards, naming conventions, Spark SQL development rules |
| all | None | Execute both syntax and specification checks |
Default: syntax + spec mode (no external dependencies required).
Prerequisites
1. Python Requirements
- Python >= 3.8
- No additional packages required (standard library only)
2. Security Rules
- This skill performs static SQL analysis only, no cluster connection required
- SQL text is processed locally, no data is sent externally
- No credentials or authentication required
Workflow
Step 1: Receive Input
Receive the SQL statement and check mode from the user. If no mode is specified, default to syntax + spec.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/report_template.md 901 B
- references/acceptance-criteria.md 2.0 KB
- references/ast-schema.md 7.2 KB
- rules/grammar_rules.py 27 KB runs code
- rules/keywords.py 22 KB runs code
- rules/perf_rules.yaml 2.5 KB
- rules/spec_rules.yaml 11 KB
- rules/syntax_rules.yaml 6.5 KB
- scripts/spark_sql_checker.py 73 KB runs code
- scripts/spark_sql_parser.py 45 KB runs code
- scripts/spark_sql_tokenizer.py 21 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 257 lines · 77 tokens per session scan A 9c002136fab4
huawei-cloud-mrs-spark-sql-check is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (49 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 2,844 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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