SKILLEMALL.ai
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AC browser-automation

Automate any web browser task with OpenClaw's built-in Playwright browser control. Use when: (1) scraping dynamic pages, (2) filling forms and submitting, (3) taking screenshots or PDFs, (4) clicking through multi-step flows, (5) monitoring changing web content, (6) automating logins. Triggers on phrases like browse this, scrape, automate web, fill form, take screenshot, click this button, browser control, open webpage.

ClawHub Agent Skills author: Fuzzyb33s v1.0.0 MIT-0 2 files body ≈ 2 260 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

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

ReferencePlaywrightInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
53/100
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: 2. 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

    • 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. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (browser-automation) differs from the folder (fuzzy-browser-automation)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 8 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 2260 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 423: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 8 items
    • +4Has examples (19 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    This is a useful browser automation skill, but it gives agents broad control over real logged-in browser sessions without enough safety boundaries.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026