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AC grok-image-generation

Generate new images and edit existing images with xAI Grok Imagine from a local OpenClaw workspace. Use when the user wants Grok/xAI as the image source for prompt-based image generation, batch variations, reference-image edits, style transfer, cleanup, background replacement, product art, poster concepts, or reusable local automation around xAI image APIs.

ClawHub Agent Skills author: Stanislav Stankovic v1.0.0 MIT-0 5 files body ≈ 712 tokens Open the sourceclawhub.ai↗ analyzed 25 h ago

Generate new images and edit existing images with xAI Grok Imagine from a local OpenClaw workspace.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions

GeneratorAI and agentsWriting and documentstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
50
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: 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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 712 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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 359: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This is a straightforward xAI image-generation helper that sends user prompts and selected images to xAI and saves returned files locally, with no hidden or destructive behavior found.
    LLM: benign (high) · VirusTotal: · 6 Jun 2026