SKILLEMALL.ai

AC test-data-gen

Generate realistic test data for software testing. Create structured data sets with Chinese locale support (names, ID cards, phone numbers, addresses, bank cards). Generate SQL inserts, JSON fixtures, CSV files, and API-ready payloads. Supports Faker (Python), custom generators, and data masking/desensitization. Use when: (1) generating test data for databases or APIs, (2) creating data fixtures, (3) building test user accounts, (4) generating Chinese locale data (身份证/手机号/姓名/地址), (5) data masking/desensitization for test environments, (6) "造数据", "测试数据", "生成数据", "假数据", "mock数据", "数据脱敏", "身份证号", "手机号生成", "Faker". NOT for: production data management, data analysis, or database administration.

ClawHub Agent Skills author: zhanghengyi1986-afk v1.0.0 MIT-0 3 files body ≈ 1 732 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 53/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency

GeneratorSoftware developmentInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
C
53/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 53/100

    • 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
    • 40Consistency. Frontmatter name (test-data-gen) differs from the folder (qa-test-data-gen)
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 6 steps
    • 100Execution cost. Instruction body is 1732 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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 698: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 6 items
    • +3Output format is stated explicitly
    • +4Has examples (11 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: clean
    This is a local test-data generator with no hidden access, but users should be careful with its production-data masking examples.
    LLM: benign (high) · VirusTotal: · 29 May 2026