Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design.

MITAuto-check passedEducation

Install AI Slop

skills CLI
$ npx skills add waybarrios/opencode-power-pack --skill ai-slop -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install waybarrios/opencode-power-pack ai-slop --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/waybarrios/opencode-power-pack.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-slop .claude/skills/ai-slop && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-slop
GitHub stars
534
Token cost
~3.6k tokens
SKILL.md length
1,783 words
Files
1
Skills in repo
32
Repo updated
First seen
Licence
MIT

At a glance

Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design.

  • Works in 12 steps: Core definition → Two quick tests → Evaluation dimensions (score each 0-4: 0… → …
  • Tasks that involve Quizzes and assessments
  • SKILL.md covers 1. Core definition, 2. Two quick tests, 3. Evaluation dimensions… and 4. Cross-signal rule and…, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Slop is an agent skill from waybarrios/opencode-power-pack. Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside frontend-design.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education, covering Quizzes and assessments, Landing pages and E-commerce operations. The repository describes itself as: 54 rigorous skills for Codex, OpenCode, and Pi: code review, security audit, feature development, frontend design, MCP tools, Hugging Face ML/training, and more. The licence is MIT.

When your agent uses it

  • Tasks that involve Quizzes and assessments
  • Tasks that involve Landing pages
  • Tasks that involve E-commerce operations

Example prompts

  • “t make AI slop”
  • “/ai-slop”

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Core definition
  2. Two quick tests
  3. Evaluation dimensions (score each 0-4: 0 excellent, 1 minor, 2 noticeable, 3 serious, 4 blocking)
  4. Cross-signal rule and severity
  5. Required project artifacts
  6. Review pipeline
  7. Scoring
  8. Repair protocol
  9. Anti-overcorrection
  10. Stable principles vs. temporal trends
  11. Definition of done
  12. Compact instruction (use under tight context)

What it can do on your machine

Read from SKILL.md and the folder at commit 9dccb6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Slop loads about 3.6k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 1,783 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from waybarrios/opencode-power-pack at commit 9dccb6d, republished under its MIT licence (© waybarrios). 1,783 words, ~3,635 tokens.

Download SKILL.mdSave it as .claude/skills/ai-slop/SKILL.md (or your agent's skills folder).
name
ai-slop
description
Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside frontend-design.
license
MIT (modified; see UPSTREAMS.json)

Scope: marketing sites, product interfaces, dashboards, portfolios, editorial pages, and e-commerce. Complements frontend-design: that skill builds the interface, this one is the detailed rubric for judging whether the result is generic ("AI slop") or genuinely fit for the product.

This is not a universal style guide. It does not ban a visual style merely because AI systems use it often — a gradient, card grid, serif headline, glass surface, or dark theme may be appropriate. It becomes a slop signal when used by reflex rather than because the product, audience, content, interaction, or brand requires it.

MUST/MUST NOT are required for acceptance; SHOULD/SHOULD NOT are default rules whose deviations need a written rationale; MAY is optional and context-dependent.

1. Core definition

AI slop is superficially competent output that lacks sufficient intention, grounding, specificity, coherence, truthfulness, or product fit. It usually shows up as one or more of:

  1. Default-driven — recognizable model/template reflexes replace deliberate decisions.
  2. Interchangeable — could be relabeled for another product with minimal change.
  3. Ungrounded — content, claims, visuals, or features aren't supported by the brief or evidence.
  4. Incoherent — polished elements that don't form one consistent system.
  5. Decorative without purpose — effects attract attention without clarifying hierarchy, meaning, state, or action.
  6. Quantity-over-value — extra sections/cards/copy exist mainly to look complete.
  7. Unreviewed — obvious responsive, accessibility, factual, or interaction defects remain.
  8. Overfitted to current AI aesthetics — follows the fashionable model output distribution rather than the project's needs.

Output is not slop merely because AI helped produce it — a result can be AI-assisted and still strong when it's grounded in real product/user context, deliberately art-directed, specific, factually honest, coherent, accessible, edited, browser-validated, and hard to transplant unchanged to another product. Human-made work can also be slop; the classification concerns the output, not proof of authorship.

2. Two quick tests

  • Substitution test: could the product name, logo, and accent color be swapped while 80% of the page stays equally plausible for another product? Yes = strong slop risk; No = likely meaningfully tied to the product.
  • Rationale test: every prominent decision MUST answer at least one of: what user need does this serve, what product truth does it express, what hierarchy does it clarify, what brand trait does it embody, what interaction state does it communicate, what constraint made it appropriate? If the only answer is "it looks modern" or "AI suggested it," it's a slop candidate.

3. Evaluation dimensions (score each 0-4: 0 excellent, 1 minor, 2 noticeable, 3 serious, 4 blocking)

  • Product grounding & specificity — generic value props, checklist-driven sections, invented features/personas, visual metaphors unrelated to the product. Evidence: a PRODUCT.md (or equivalent) with explicit users, jobs, constraints, and a traceable reason per section. Repair: cut unsupported sections, rewrite around concrete user tasks.
  • Truthfulness & evidence (blocking) — invented prices, metrics, testimonials, logos, citations, or controls that imply unavailable functionality. Use [NEEDS INPUT], "Price on request," or clearly labeled sample data instead; keep an evidence ledger for verifiable claims; mark fictional demo data as demo data.
  • Information architecture & narrative — default hero → logo cloud → cards → metrics → testimonials → pricing → FAQ; shuffleable section order; front-loaded slogans. Repair: define the reader's questions in order, give each section a unique job, cut sections that don't advance understanding.
  • Composition & layout — everything centered, identical repeated cards, uniform spacing, excessive pills, desktop merely stacked on mobile, heading overflow at mid widths. Repair: content-led layouts, intentional density variation, a spatial system broken only for a reason, test real content at multiple widths.
  • Visual system coherence — inconsistent radii/shadows/icon weights, unrelated surface treatments per section, tokens defined but bypassed. Evidence: a DESIGN.md with semantic tokens (color, type, spacing, radius, elevation, motion) that components actually consume, and a documented exception when something breaks the system.
  • Color & material — unexplained purple/violet "tech" gradients, cyan glows on dark backgrounds, gradient headline text without purpose, glassmorphism everywhere, palette chosen from category stereotype alone. None of these are automatically forbidden — flag them when repeated, unsupported by the brand concept, or chosen as a reflex.
  • Typography — same popular default typefaces everywhere, one weight pattern for every role, oversized display type for "editorial" feel, tiny tracked eyebrow labels, flat hierarchy, hero text that fails on mobile. Repair: select type from brand attributes and reading conditions, define role-based type tokens, test long words/localization/zoom.
  • Copy & voice — "revolutionize/unlock/seamless/elevate," "not just X, but Y," empty claims ("built for the future"), repetitive cadence and em dashes, "Learn more" buttons with a predictable destination. Repair: concrete nouns/verbs, state what the product does for whom and why, remove undemonstrable claims.
  • Imagery & iconography — generic gradient blobs standing in for the product, unlicensed stock hotlinks, AI imagery with anatomy/lighting inconsistencies, mixed icon families. Repair: authentic product imagery, purpose-built illustration, real screenshots, or honest placeholders — define an art-direction rule before generating assets.
  • Motion & interaction — every element fades/rises on scroll, indiscriminate bounce easing, long entrance sequences, parallax with no semantic purpose, motion that ignores reduced-motion preferences. Repair: write a motion rationale, use a small tokenized duration/easing system, test keyboard/touch/reduced-motion/low-performance conditions.
  • Usability & accessibility (blocking signals) — keyboard traps, missing focus states, insufficient contrast, unlabeled controls, tiny targets, hover-only information, broken zoom/reflow. Baseline: WCAG 2.2 AA for ordinary public-facing work.
  • Responsive behavior — desktop grid collapsed into an undifferentiated stack, horizontal overflow, abrupt type-scale jumps, unchanged content priority on small screens. Repair: pick breakpoints from content failure (not device labels), test narrow/medium/wide plus zoom.
  • Functional completeness — dead buttons/links, forms that can't submit or report state, tabs/menus/dialogs implemented only visually, missing loading/empty/error/disabled states. Repair: browser-based task tests from the brief, including edge states; never present incomplete controls as finished.
  • Implementation quality — avoidable layout shift, unoptimized assets, excessive client JS for static content, repeated one-off CSS values, invalid semantics, console/hydration errors, performance sacrificed for decoration.
  • Distinctiveness & category reflex — could someone guess the palette/typography/hero/components from the category alone (first-order reflex)? After banning the obvious cliché, did the result just move to the next fashionable alternative, e.g. dark-purple-glass → cream-editorial-serif (second-order reflex)? A design should feel plausible for this project, not inevitable from its category.

4. Cross-signal rule and severity

Classify the result as slop when: one blocking issue exists, three or more dimensions score 3, the same reflex repeats across multiple registers (color + typography + layout + copy), or both quick tests fail.

  • Blocking (must fix before delivery): fabricated claims/facts, broken primary tasks, serious accessibility failures, deceptive controls, unusable responsive behavior, missing evidence for public claims, legal/safety-risk content.
  • Major (strongly harms quality/distinctiveness): page-wide template reflex, incoherent design system, repetitive composition, unreadable typography, purposeless motion, generic copy dominating the experience.
  • Minor (localized): one unnecessary pill, one weak label, one inconsistent radius, one overly long line, one generic section.
Show full SKILL.md (699 more words)Show less

5. Required project artifacts

  • PRODUCT.md: product/offer, target users, primary jobs, key tasks, real facts vs. assumptions, non-goals, content gaps, accessibility target, success criteria.
  • DESIGN.md: art-direction sentence, brand attributes, anti-references, extracted principles from visual references, palette and semantic color roles, typography roles, spacing/grid system, radius/border/elevation/material rules, imagery direction, motion rules, component vocabulary, responsive principles, intentional exceptions.
  • EVIDENCE.md: for every externally verifiable claim — exact claim, source, confidence, allowed wording, where it appears. Unsupported claims MUST NOT ship.

6. Review pipeline

A single self-review is insufficient. Gate through, in order:

  1. Deterministic source scan — unsupported numbers/claims, forbidden phrase patterns, off-token colors/spacing, typography violations, inaccessible semantics, small targets, missing states, broken links, console/perf defects.
  2. Browser task tests — keyboard navigation, forms/validation, menus/dialogs, error/empty states, mobile navigation, reduced motion, realistic content lengths.
  3. Screenshot review — narrow mobile, wide mobile/small tablet, laptop, wide desktop, 200% zoom where relevant; inspect hierarchy, rhythm, overflow, coherence, product fit.
  4. Visual judge — a vision-capable evaluator scores dimension-by-dimension against this rubric and the brief, with evidence tied to visible regions and a confidence level; "AI-looking" is not automatically low quality.
  5. Pairwise comparison — candidate vs. previous version, vs. a control build without this rubric, vs. one alternative art direction, or vs. a relevant reference. Prefer pairwise preference over isolated "8/10" scores.
  6. Human acceptance — does this feel made for this product? What feels generic, dishonest, or unsupported? What would be remembered tomorrow? Which decision would a competent designer challenge?

7. Scoring

Track two separate results — never collapse them into one number:

  • Shipping readiness (pass/fail): blocking defects, functional tasks, accessibility target, evidence. A visually distinctive page can still fail shipping readiness.
  • Slop risk (weighted 0-100): product grounding 15, truthfulness 15, information architecture 10, composition/layout 10, system coherence 10, copy/voice 8, typography 7, color/material 7, imagery 5, motion 5, responsive 4, distinctiveness 4. 0-14 low risk, 15-29 minor concerns, 30-49 noticeable genericity, 50-69 major slop characteristics, 70-100 dominated by slop.

8. Repair protocol

When slop is detected: name the failed dimension, cite visible/source-level evidence, identify whether the cause is missing context, a model reflex, incomplete implementation, or weak review, remove unsupported content before adding polish, fix the system or rule (not just the symptom), re-run the deterministic/browser/screenshot checks, compare pairwise against the previous version, and record what changed and why.

9. Anti-overcorrection

Do not turn "anti-slop" into another recognizable house style. Don't automatically replace dark-neon with cream-editorial, sans-serif with giant italic serif, cards with arbitrary asymmetry, gradients with flat beige, polished copy with forced quirkiness, or standard layouts with scroll gimmicks. The goal isn't to look less like one AI default by adopting another — it's decisions justified by the project.

Separate rules into two layers so the rubric doesn't fight yesterday's cliché while ignoring tomorrow's:

  • Stable (no expiry): interchangeability/substitution, product grounding, truthfulness, coherence, accessibility, functional completeness, rationale, distinctiveness-from-stereotype.
  • Temporal (contextual, must expire): give each an introduction date and a review date, e.g. dark-purple-cyan-glass-default (current LLM tech-product default — flag when used without brand rationale) or overused-default-fonts: Inter, Roboto, Geist, Plus Jakarta Sans, Space Grotesk (exception: Roboto Mono for monospace, Roboto Condensed for display). Review temporal rules at their review date; drop them if the trend faded, otherwise renew. A temporal rule MUST NOT become a new predictable default — after banning a trend, check whether the agent just adopted the next fashionable alternative instead.

11. Definition of done

No blocking issue remains; primary tasks work in the browser; claims are supported or explicitly labeled; the accessibility target is met; screenshots pass responsive review; the design is internally coherent; prominent decisions have written rationale; the substitution test doesn't reveal broad interchangeability; pairwise review prefers the final result over its control/previous version; remaining known limitations are documented.

12. Compact instruction (use under tight context)

AI slop is superficially polished but insufficiently intentional, grounded, specific, coherent, truthful, or product-fit output. Don't judge styles in isolation — detect default reflexes, interchangeability, unsupported content, system inconsistency, decorative excess, incomplete interaction, and unreviewed defects. Ground every major decision in PRODUCT.md/DESIGN.md, verify claims through EVIDENCE.md, test real browser tasks and responsive screenshots, and use pairwise review instead of a self-assigned score. A common visual treatment is fine when deliberate, coherent, accessible, and justified by the project.

© waybarrios, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ai-slop of waybarrios/opencode-power-pack.

Open the folder on GitHubat commit 9dccb6d

Compare with similar skills

AI Slop next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Slop compared with similar skills
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AI Slop this skillwaybarrios/opencode-power-pack534—~3.6kAutomated safety check: PassMIT
Auto Improvecrimeacs/auto-improve135—~651Automated safety check: PassMIT
Trust Signalsthedaviddias/Front-End-Checklist74k—~619Automated safety check: PassMIT
CloudbaseLeoYeAI/openclaw-master-skills2.2k1 repos~4.7kAutomated safety check: PassMIT
Scenario Product Shotsscenario-labs/skills946—~1.8kAutomated safety check: PassMIT
Ecommerce Landing Pagenexscope-ai/eCommerce-Skills1.1k—~617Automated safety check: PassMIT

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Questions about AI Slop

What does AI Slop do?

Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. AI Slop is an agent skill from waybarrios/opencode-power-pack. Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design.

When should I use AI Slop?

AI Slop fits situations like: tasks that involve Quizzes and assessments; tasks that involve Landing pages; tasks that involve E-commerce operations.

How do I install AI Slop in Claude Code?

Run `npx skills add waybarrios/opencode-power-pack --skill ai-slop -a claude-code`. Or copy the skill folder (skills/ai-slop in waybarrios/opencode-power-pack) into .claude/skills/ai-slop in your project. Claude Code loads it when a task matches its description.

How do I install AI Slop in Codex?

Run `npx skills add waybarrios/opencode-power-pack --skill ai-slop -a codex`. Or copy the skill folder (skills/ai-slop in waybarrios/opencode-power-pack) into .agents/skills/ai-slop in your project. Codex loads it when a task matches its description.

Can I use AI Slop in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add waybarrios/opencode-power-pack --skill ai-slop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-slop, .gemini/skills/ai-slop, .github/skills/ai-slop and .opencode/skills/ai-slop in your project.

What does AI Slop need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Slop is instructions for the agent only.

Does AI Slop access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Slop safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Slop use?

AI Slop is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Slop use?

About 3.6k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Slop?

Skills that share tags, products or a category with AI Slop: Auto Improve (crimeacs/auto-improve, 135 stars), Trust Signals (thedaviddias/Front-End-Checklist, 74k stars), Cloudbase (LeoYeAI/openclaw-master-skills, 2.2k stars) and Scenario Product Shots (scenario-labs/skills, 946 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Slop?

waybarrios (a GitHub user) maintains it in waybarrios/opencode-power-pack, which has 534 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 6, 2026.

Source: waybarrios/opencode-power-pack on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.