agentsclimarketplace

Image review

Skill vikynofebriputra-creator/Orkas-Awesome-AgentSkills/creation/skills/image-review

Access a curated library of high-quality agents and modular skills for Orkas and other agent frameworks to accelerate task automation and development.From the repository description

Install
npx -y skills add vikynofebriputra-creator/Orkas-Awesome-AgentSkills --skill image-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.6 KB, 616 tokens by cl100k_base, as published. Nobody here has run it

Image Review

When to Use

Use this skill when the user provides an image or image path and asks whether it is good enough, matches the prompt, or needs regeneration/editing.

Use especially when the image includes:

  • text, logo-like elements, product details, people, hands, faces, or complex layouts.
  • commercial, brand, social, presentation, ad, ecommerce, or print use.
  • reference-image consistency requirements.
  • safety, copyright, privacy, or misleading-content risks.

Do not invent an image review if the image cannot be viewed or read. Ask for an upload/path or state that only a generic checklist can be provided.

Workflow

  1. Gather context:
    • original user request, prompt, target use, platform/format, reference images, and output image.
  2. Inspect what is visible:
    • subject, composition, text, lighting, colors, artifacts, crop, resolution, and file constraints.
  3. Score the image:
    • Prompt adherence.
    • Technical quality.
    • Composition and aesthetics.
    • Fitness for target use.
    • Text and detail reliability.
    • Reference consistency.
    • Safety, copyright, privacy, and brand risk.
  4. Separate objective issues from subjective preferences.
  5. Preserve successful elements in any revision advice.
  6. Recommend the smallest next action:
    • accept as-is.
    • local post-process.
    • local/AI edit.
    • regenerate with revised prompt.
    • ask for missing source/reference material.

Return Format

## Image Review

### Verdict
Accept / Minor post-process / Local edit / Regenerate / Need more context

### Scores
- Prompt adherence:
- Technical quality:
- Composition:
- Target-use fit:
- Text/detail reliability:
- Reference consistency:
- Safety/copyright/privacy:

### Issues
1. Issue:
   Evidence:
   Fix:

### Revised Prompt Or Edit Brief
...

### Keep
- Elements that already work:

### Next Step
...

Dependencies

  • The image must be visible or readable.
  • Prompt/source brief is needed for prompt adherence review.
  • Reference images are needed for consistency review.

Limits and Known Issues

  • Aesthetic judgment is partly subjective; do not present taste preferences as hard failures.
  • Do not give legal clearance for copyright, trademark, publicity rights, or advertising claims.
  • Do not provide instructions to bypass model safety systems.
  • Do not claim exact generation causes unless they are visible from the image or provided by the user/tool logs.

Examples

User: "这是刚生成的产品海报,能不能发?"

Handling: review title readability, product prominence, crop, artifacts, platform fit, brand risk, and whether local post-process or regeneration is the right next step.

User: "这张图和 prompt 匹配吗?"

Handling: compare subject, style, composition, colors, text, and constraints against the prompt; return mismatches and a revised prompt that keeps the successful elements.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most review quality skills give in 616 tokens

Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07

  • Ask questions one at a timein 81 of 1048, across 64 files
  • Provide a recommended answer for each questionin 73 of 1048, across 50 files
  • Explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
  • Resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
  • Interview the user relentlessly about the planin 38 of 1048, across 13 files
  • Order findings by severityin 31 of 1048
  • Resolve each branch of the decision treein 27 of 1048, across 5 files
  • Run a grilling sessionin 26 of 1048, across 5 files
  • Update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
  • Propose precise canonical terms for vague languagein 25 of 1048, across 7 files
  • Create documentation files lazilyin 24 of 1048, across 5 files
  • Assign severity to every findingin 24 of 1048

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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