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LLM 驱动的数仓元数据自动治理。解决企业数仓最普遍的痛点：字段无注释、表无描述、命名混乱、NL2SQL 准确率低。 自动探测连接模式：pyhive 可用时读写 Hive；否则通过 MCP 工具只读，结果持久化到 SQLite。 触发时机（以下任一即触发）： - 用户提到：数据治理、元数据、字段注释、表备注、字段没有描述、数仓治理、data governance - 用户提到：NL2SQL 准确率低、字段语义不清、新人看不懂表结构 - 用户运行 /data-governance 命令 - 用户想批量生成或补全数仓字段说明

ClawHub Agent Skills author: rickitsui v1.0.2 MIT-0 5 files body ≈ 1 239 tokens Open the sourceclawhub.ai↗ analyzed 19 h ago

LLM 驱动的数仓元数据自动治理。解决企业数仓最普遍的痛点：字段无注释、表无描述、命名混乱、NL2SQL 准确率低。 自动探测连接模式：pyhive 可用时读写 Hive；否则通过 MCP 工具只读，结果持久化到 SQLite。 触发时机（以下任一即触发）： -…

As a process F 28/100 · Will not run — References files that are not bundled: references/architecture.md

ProcedureAI and agentstype 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
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
F
28/100
Will not run
References files that are not bundled: references/architecture.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 0. 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")
  • warning missing-ref reference to a missing file: references/architecture.md

Process rating: all ten parameters 28/100

Will not run. References files that are not bundled: references/architecture.md
  • 0Tools and files. 1 referenced file(s) missing: references/architecture.md
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (data-governance) differs from the folder (warehouse-meta)
  • 100Steps. 7 steps
  • 100Execution cost. Instruction body is 1239 tokens

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 264: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 7 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
This skill is purpose-aligned for warehouse metadata governance, but it can read sensitive warehouse samples and automatically write LLM-generated comments back to Hive at scale without a separate approval step.
LLM: suspicious (high) · 12 Jun 2026