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AC selenium-skill

Generates production-grade Selenium WebDriver automation scripts and tests in Java, Python, JavaScript, C#, Ruby, or PHP. Supports local execution and TestMu AI cloud with 3000+ browser/OS combinations. Use when the user asks to write Selenium tests, automate with WebDriver, run cross-browser tests on Selenium Grid, or mentions "Selenium", "WebDriver", "RemoteWebDriver", "ChromeDriver", "GeckoDriver". Triggers on: "Selenium", "WebDriver", "browser automation", "Selenium Grid", "cross-browser", "TestMu", "LambdaTest".

LambdaTest/agent-skills Agent Skills author: LambdaTest 12 files · 1 script body ≈ 2 105 tokens Open the sourcegithub.com↗ analyzed 33 h ago

Generates production-grade Selenium WebDriver automation scripts and tests in Java, Python, JavaScript, C, Ruby, or PHP.

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorPlaywrightSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "languages"

    Process rating: all ten parameters 58/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 5 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2105 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +3Description length 522: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (9 of 9)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.