SKILLEMALL.ai

CD data-classification

用于数据分类、数据分级、数据分类分级任务。用户要求对单一数据字段名、字段列表、数据库表 SQL/DDL 文件进行数据分类、数据分级或数据分类分级时使用;支持普通数据分类分级、GB/T 43697-2024 通用数据分类分级、金融数据分类分级、JR/T 0197-2020 金融数据安全级别,以及“通用数据标签 + 金融数据标签”的金融双标签体系。

Not recommendedcritical or high security findings
ClawHub Agent Skills author: liangzai v1.0.0 MIT-0 6 files · 1 script body ≈ 1 734 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

用于数据分类、数据分级、数据分类分级任务。用户要求对单一数据字段名、字段列表、数据库表 SQL/DDL 文件进行数据分类、数据分级或数据分类分级时使用;支持普通数据分类分级、GB/T 43697-2024 通用数据分类分级、金融数据分类分级、JR/T 0197-2020 金融数据安全级别,以及“通用数据标签 +…

As a process D 48/100 · Unfinished process — References files that are not bundled: references/jrt0197-appendix-a-full.csv

ProcedureData and analyticsWriting and documentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
C
80/100
safety, quality, tests
Safety 60%
82
Quality 40%
76
Run on models
none yet
Process rating
D
48/100
Unfinished process
References files that are not bundled: references/jrt0197-appendix-a-full.csv
Tools and files w 18
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  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:51
    Instruction to hide actions from the user
    For a **SQL file/table**, classify **all parsed columns from all tables**. Choose the delivery format internally and do not tell the user the threshold/routing rule.

Files scanned: 6. 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/jrt0197-appendix-a-full.csv

Process rating: all ten parameters 48/100

Will not run. References files that are not bundled: references/jrt0197-appendix-a-full.csv
  • 0Tools and files. 1 referenced file(s) missing: references/jrt0197-appendix-a-full.csv
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 17 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 49 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1734 tokens
  • 100Progress reporting. Reports progress

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 173: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 49 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: clean
This is a local data-schema classification skill whose main behavior matches its stated purpose, though users should handle exported CSVs and schema metadata carefully.
LLM: benign (high) · VirusTotal: · 16 Sept 2026