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AC data-analysis-sql

大数据开发工程师级数据分析与SQL技能。(1)多数据引擎SQL编写(Hive/SparkSQL/Presto/ClickHouse/Doris/MySQL/PG/BigQuery)。(2)复杂SQL改造调试与性能优化。(3)数仓建模(ODS/DWD/DWS/ADS)维度设计/SCD变更。(4)数据探查/指标设计/ETL管线编排。(5)数据质量检测与异常分析。(6)SQL改写(方言迁移/语法适配)。(7)UDF/UDTF开发规范。(8)表结构文档自动生成与迁移支持。(9)知识库目录生成与维护(schema/metrics/relations/enums)。触发:写SQL/改SQL/数仓建模/ETL/SQL优化/数据质量/指标设计/整理文档/生成md/迁移文档/知识库

ClawHub Agent Skills author: whisky v1.0.4 MIT-0 17 files body ≈ 710 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerMySQLGoogle CloudData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 39 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 710 tokens
  • 100Running it twice. No mutating operations

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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 336: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 39 items
  • +4Reference files are cited in the instructions (11 of 11)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This SQL and data-documentation skill is purpose-aligned and locally scoped, with only ordinary caution needed around broad triggers and file overwrites.
LLM: benign (high) · VirusTotal: · 29 May 2026