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AC visual-prompt-engine

Generate diverse, non-repetitive image prompts powered by real visual references from Dribbble and design platforms. USE WHEN: user wants an image prompt, needs creative visual inspiration, asks for design-informed prompts, wants to avoid repetitive AI image generation, or says 'generate a prompt for an image', 'give me a creative image idea', 'make me a unique visual prompt'. DON'T USE WHEN: user wants to generate the image itself (use an image generation tool), wants to edit an existing image, or needs text-only content. EDGE CASES: 'make me an image' → use image generation tool, then optionally this skill for the prompt. 'improve this image prompt' → this skill. 'I keep getting similar AI images' → this skill (solves repetition).

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files body ≈ 1 005 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate diverse, non-repetitive image prompts powered by real visual references from Dribbble and design platforms.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
95
Run on models
none yet
Process rating
C
50/100
Has gaps
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

    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: 9. 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 50/100

    • 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
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Steps. 11 steps, 4 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1005 tokens
    • 100Running it twice. Mutating operations check current state

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 742: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.