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AC gpt-image-gen

Generate images using ChatGPT's GPT-Image-2 model via browser automation (CDP). Shares the user's daily Brave Browser (port 9222) via the brave-browser-agent skill. Navigate to chatgpt.com, input a prompt, trigger generation, and extract the result. Use when: (1) User asks to generate/create/draw an image or picture using GPT/ChatGPT (2) User mentions "GPT生成图片", "ChatGPT画图", "gpt-image" (3) Need high-quality AI image generation via ChatGPT (4) User wants photorealistic images, complex scenes, or text-in-image generation (5) Fallback/complement to Gemini image generation Requires: Brave Browser running with --remote-debugging-port=9222, ChatGPT logged in, Python 3, websockets pip package.

ClawHub Agent Skills author: mayf3 v1.0.0 MIT-0 3 files body ≈ 1 326 tokens Open the sourceclawhub.ai↗ analyzed 24 h ago

Generate images using ChatGPT's GPT-Image-2 model via browser automation (CDP).

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

GeneratorAI 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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: 3. 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 56/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1326 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
    • -2localhost URLs: will not work for another user
    • -240 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 698: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (12 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    The skill is aimed at image generation and shows no malware behavior, but it asks to control the user's everyday logged-in Brave browser, which is broader access than this task needs.
    LLM: suspicious (high) · VirusTotal: · 5 Jun 2026