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BD gui-agent

GUI automation via visual detection. Clicking, typing, reading content, navigating menus, filling forms — all through screenshot → detect → act workflow. Supports macOS and Linux.

ClawHub Agent Skills author: AlfredJamesLi v1.0.1 MIT-0 65 files · 1 script body ≈ 469 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsInfrastructuretype 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
B
85/100
safety, quality, tests
Safety 60%
98
Quality 40%
65
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token memory/apps/gnome-terminal/meta.json:80
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "Crea…a-e",
    quoted
  • low Secrets in code secret-high-entropy-token memory/apps/gnome-terminal/meta.json:158
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "Pres…-c_\"",
    quoted

Files scanned: 48. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 49/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (gui-agent) differs from the folder (gui-claw)
  • 100Tools and files. No external tools needed
  • 100Steps. 8 steps
  • 100Execution cost. Instruction body is 469 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)
  • +3Output format is not stated: the model decides each time
  • -46 reference files, but SKILL.md never points to them: the model will not open them
  • -39 of 10 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 179: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: suspicious
This is a real GUI automation skill, but it gives an agent broad screen, keyboard, memory, and remote-VM control with some under-scoped execution paths users should review carefully.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026