Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add waybarrios/opencode-power-pack --skill ai-slopgit clone --depth 1 https://github.com/waybarrios/opencode-power-packWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/waybarrios/opencode-power-pack/ai-slop)<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/ai-slop"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/ai-slop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/ai-slop"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/ai-slop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 104 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.02986 |
| Opus 5.5 | $0.00024 | $0.01194 |
| Sonnet 5.5 | $0.00012 | $0.00597 |
| Haiku 4.5 | $0.00006 | $0.00299 |
Grade A, and why
ai-slop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Default-driven — recognizable model/template reflexes replace deliberate decisions.
- Interchangeable — could be relabeled for another product with minimal change.
- Ungrounded — content, claims, visuals, or features aren't supported by the brief or evidence.
- Incoherent — polished elements that don't form one consistent system.
- Decorative without purpose — effects attract attention without clarifying hierarchy, meaning, state, or action.
- Quantity-over-value — extra sections/cards/copy exist mainly to look complete.
- Unreviewed — obvious responsive, accessibility, factual, or interaction defects remain.
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 105 lines · 61 tokens per session scan A ffc396010fb7
ai-slop is a skill published in the GitHub repository waybarrios/opencode-power-pack (544 stars, last pushed 2d ago), licensed MIT. It adds 61 tokens to every session and 2,986 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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