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Full Computer Use for OpenClaw via kasmweb/chrome Docker sidecar. Navigate any website, click, type, fill forms, extract data, upload files, screenshot on any platform including private authenticated accounts. Principal logs in once via noVNC. Sessions saved permanently in Docker volume. After one-time manual login via noVNC, agent can access authenticated platforms. CapSolver solves CAPTCHAs automatically. Browserbase profile available for residential proxy and stealth. Claude vision analyses screenshots and AI-generated images natively. Every action logged. Every discovery improves performance via .learnings/.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 5 files body ≈ 3 866 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Full Computer Use for OpenClaw via kasmweb/chrome Docker sidecar.

As a process D 43/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

ProcedureDockerPlaywrightTelegramCloudflareInfrastructureAI and agentstype 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
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
80
Run on models
none yet
Process rating
D
43/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-privilege browser_control.py:97
      Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)
      args=["--no-sandbox", "--disable-setuid-sandbox", "--disable-dev-shm-usage"]
      detectorcode literal

    Files scanned: 5. 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 43/100

    • 0Steps. Prose only: no discrete steps
    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3866 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 15 top-level sections: this looks like several domains in one skill

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +2Single-language instructions
    • +3Description length 619: enough signal without eating the budget
    • +4Structure: 16 headings
    • +4Has examples (13 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.