SKILLEMALL.ai

AB qa-engineer-assistant

This skill should be used when the user is a QA/test engineer needing help with any testing task. Covers the full testing workflow: understanding requirements, designing test cases, writing API automation scripts (Python/pytest/requests), writing UI automation scripts (Playwright/Selenium), generating bug reports, and providing guidance for junior testers. Trigger when the user mentions: test cases, test plan, API testing, interface testing, UI automation, bug report, regression test, test coverage, pytest, Selenium, Playwright, 测试用例, 接口测试, UI自动化, Bug报告, 测试计划, 冒烟测试, 回归测试.

ClawHub Agent Skills author: guolongganga v1.0.0 MIT-0 6 files body ≈ 1 300 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

IntegrationPlaywrightData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Tools and files w 18
60
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: 6. 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 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 49 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1300 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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 578: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 49 items
    • +3Output format is stated explicitly
    • +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: 90.

    External checks

    ClawHub: clean
    This QA testing helper is coherent and purpose-aligned, but users should treat its generated API tests as real network code and handle test credentials carefully.
    LLM: benign (high) · VirusTotal: · 29 May 2026