SKILLEMALL.ai

DC alibabacloud-lakehouse-migration

Unified dispatch entry point for the LHM (Lakehouse Migration Center) skill, coordinating the three core capabilities of SQL conversion, data validation, and big-data workflow migration. Use when the user raises a lakehouse migration requirement, such as SQL dialect conversion (e.g., "presto to spark", "hive to maxcompute", "转换 SQL", "SQL 转换", "方言转换"), pre/post-migration data validation / data consistency comparison ("数据校验", "数据比对", "一致性检查", "跑校验", "数据量校验", "指标校验"), migration of scheduled workflows such as DataWorks/DolphinScheduler/Airflow ("工作流迁移", "调度任务迁移", "建数据源", "创建数据源", "把 X 迁移到 Y"), or when the user is unsure which sub-skill to use or needs cross-skill collaboration.

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: alibabacloud v0.0.1 MIT-0 80 files body ≈ 8 291 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Unified dispatch entry point for the LHM (Lakehouse Migration Center) skill, coordinating the three core capabilities of SQL conversion, data validation, and…

As a process C 55/100 · Has gaps — References files that are not bundled: references/manifest.json, references/ram-policies.md, scripts/requirements.txt

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
D
74/100
safety, quality, tests
Safety 60%
82
Quality 40%
62
Run on models
none yet
Process rating
C
55/100
Has gaps
References files that are not bundled: references/manifest.json, references/ram-policies.md, scripts/requirements.txt
Tools and files w 18
0
Result and completion w 14
0
Execution cost w 6
40
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. 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 · 1

  • high Concealment en-hide-from-user SKILL.md:222
    Instruction to hide actions from the user
    - Installing or running any sub-skill CLI (`uv tool install`, `pip install`, `lhm-sch-ds`, `lhm-sch-env`, `lhm-sch-deploy`, `lhm-sch-read-exec`, the data-validation scripts, `lookup_rules.py`). **A su

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8291 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/manifest.json
  • warning missing-ref reference to a missing file: references/ram-policies.md
  • warning missing-ref reference to a missing file: scripts/requirements.txt
  • warning missing-ref reference to a missing file: scripts/install_lhm_plugin.sh

Process rating: all ten parameters 55/100

Will not run. References files that are not bundled: references/manifest.json, references/ram-policies.md, scripts/requirements.txt
  • 0Tools and files. 4 referenced file(s) missing: references/manifest.json, references/ram-policies.md, scripts/requirements.txt
  • 0Result and completion. Does not say what the result is
  • 40Execution cost. Instruction body is 8291 tokens: crowds the task out of the window
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 88 steps, 1 vague phrases
  • 100Failures and branches. 7 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 8 example trigger phrases
  • +3Description length 683: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 88 items
  • +4Has examples (18 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This LHM migration dispatcher is coherent, but it automatically updates executable cloud tooling and silently sends SQL conversion content as audit data, so it should be reviewed before installation.
LLM: suspicious (high) · VirusTotal: · 9 Oct 2026