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Slop Cop

作者 Chad Keith · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install slop-cop
功能描述
Judges visual design assets and AI-generated images before they ship. Use when the user wants to compare design options, choose between asset variants for a...
使用说明 (SKILL.md)

slop-cop

A visual-design referee. Given one or more image assets plus a decision context, produce strict per-asset verdicts (SHIP, FIX, or KILL) and, when multiple candidates compete for one slot, a ranked recommendation with placement reasoning.

The goal: stop hallucinated text, melted hands, off-brand vibes, and obvious AI artifacts from reaching production.

When to invoke

  • User has 1–N images and a decision to make ("which works best for hero?", "is this safe to ship?", "does this fit my brand?").
  • User wants a second opinion on a visual choice before deploy.
  • User asks to audit a landing page or compare AI-generated variants.
  • User explicitly says "slop check" / "is this AI slop?"

Inputs the skill needs

Before analysis, confirm or infer:

  1. Image paths — 1 or more local file paths or URLs.
  2. Decision context — what slot/role is this for? Examples: "hero banner at 1200x600", "square avatar 1024x1024", "mobile card at 4:5", "is this safe to ship anywhere?".
  3. Target render size / aspect ratio — if relevant (e.g. hero rendered at 600x600 with rounded corners and a 4px border).
  4. Brand palette / style — hex colors and a one-line style descriptor when available (e.g. "navy #0f3a66 / orange #f3812a, cartoon illustration").
  5. Mode — single-asset audit (SHIP/FIX/KILL) or comparative pick (rank + recommend one).

If the user does not provide brand context, ask once. If they decline, proceed without brand-fit scoring and note it in the verdict.

Workflow

1. Run the vision pass

For each image, call the OpenClaw image tool with the strict checklist prompt in references/vision-prompt-template.md. Pass one image per call when possible — keeps the model focused. Use images (multi) only for explicit side-by-side comparison once each has been individually vetted.

The prompt template forces the vision model to enumerate findings against a fixed checklist instead of writing vibes-based prose.

2. Score against the full checklist

The mandatory checklist lives in references/checklist.md. Every asset must be scored on:

  • Hallucination scan — gibberish text, extra/melted fingers, broken anatomy, duplicated objects, watermarks, AI signatures, lighting that contradicts itself.
  • Legibility at target size — can any text on the asset be read at its actual render size?
  • Responsive safety — will the focal subject survive cropping to 16:9, 4:5, 1:1, and 9:16? Identify the focal point in pixel/percent terms.
  • Cross-browser / format — transparency needs (PNG/WEBP vs JPG), color profile concerns (sRGB vs P3), iOS Safari quirks.
  • Brand fit — if palette/style provided, check coherence; flag major mismatches.
  • Format / size sanity — actual dimensions, file size for web, aspect-ratio fit for the target slot.

3. Assign a verdict per asset

Use exactly one verdict word per asset, plus a one-sentence reason. No hedging, no "looks okay but...".

Verdict Meaning
SHIP Clean. Deploy as-is.
FIX Salvageable with a specific edit (crop, recolor, regenerate text region, swap to different aspect). State the fix.
KILL Do not use. Hallucination, off-brand, broken anatomy, or wrong-tool-for-the-job.

Hard kill triggers (any one of these = automatic KILL):

  • Visible hallucinated/gibberish text on a graphic shipping to prod.
  • Extra/missing/melted fingers on a human or human-adjacent character.
  • Visible watermark or AI-tool signature.
  • Major brand-palette violation (when palette provided) that can't be fixed by recolor.

See references/anti-patterns.md for the full kill list and CSS-level gotchas that come up on real sites.

4. Comparative mode (multiple candidates, one slot)

When the user is choosing between assets for a single slot:

  1. Verdict each candidate individually first.
  2. Drop all KILL verdicts from the running.
  3. Rank remaining SHIP and FIX candidates by fit-to-context (brand match > focal-point survival > legibility > polish).
  4. Recommend one. Name the file path, the slot, and one-sentence placement reasoning.
  5. If every candidate is KILL or FIX, recommend regeneration with a brief brief.

5. Output format

Return a structured response:

## Verdicts
- \x3Cfilename> — VERDICT — one-sentence reason
- \x3Cfilename> — VERDICT — one-sentence reason
...

## Anti-patterns flagged
- (optional) bullet list of CSS/HTML/format gotchas detected from context

## Recommendation
\x3CFor comparative mode: which file goes in which slot, why, and any FIX steps needed before deploy.>

## Deploy notes
\x3CConcrete file paths, target dimensions, format conversions, and any CSS/HTML lines that should change. Do NOT execute deploys — describe them.>

Keep it tight. No filler, no "great question."

Failure modes

  • Vision tool unavailable / errors out — Document the failure, then make a best-effort judgment from filename, file metadata (identify / file / exiftool if available), and decision context. Mark the verdict as BEST-EFFORT in parens and flag that a manual eyeball is required before ship.
  • No brand context provided — Proceed; note "no brand check performed."
  • Asset is a wordmark/logo — Skip hallucination scan for stylized typography (intentional design ≠ gibberish), but still check legibility, format, and brand-palette match.

References

安全使用建议
Safe to install for visual asset or design review. Users should invoke it when they actually want an image, mockup, banner, icon, or visual choice evaluated, and should ignore it if it activates on non-visual deployment questions.
能力评估
Purpose & Capability
The supplied evidence describes an image/design-audit skill for judging visual quality or deployment readiness; no purpose-mismatched capability, destructive action, exfiltration, or account mutation is shown.
Instruction Scope
The trigger wording is somewhat broad and colloquial, which could cause the skill to activate for loosely related requests, but this is a routing-quality concern rather than a security concern.
Install Mechanism
No risky installer, executable payload, package hook, or hidden setup behavior is indicated in the supplied scan context.
Credentials
The described visual-audit purpose does not imply broad local indexing, credential use, or sensitive environment access, and no such access is shown by the provided evidence.
Persistence & Privilege
No persistence, privilege escalation, background worker, or automatic long-running behavior is indicated.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install slop-cop
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /slop-cop 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Initial release: visual QA skill that catches AI-generated slop before it ships to prod.
元数据
Slug slop-cop
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Slop Cop 是什么?

Judges visual design assets and AI-generated images before they ship. Use when the user wants to compare design options, choose between asset variants for a... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 45 次。

如何安装 Slop Cop?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install slop-cop」即可一键安装,无需额外配置。

Slop Cop 是免费的吗?

是的,Slop Cop 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Slop Cop 支持哪些平台?

Slop Cop 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Slop Cop?

由 Chad Keith(@chchchadzilla)开发并维护,当前版本 v0.1.0。

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