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.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00030 | $0.01493 |
| Sonnet 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 7d 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.
- 7d 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 (490 stars, last pushed 4d ago), licensed MIT. It adds 61 tokens to every session and 2,986 once invoked, about $0.0003 per session on Opus 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.
Other skills, from other repositories
ln-41-test-strategy-planner
Plans a risk-based test portfolio and prioritized scenarios without editing tests. Not for test execution or implementation.
Audit PodClaw
Comprehensive audit of PodClaw — the autonomous agent system (Claude Agent SDK, Python). Use when asked to audit, review, or assess PodClaw — covering agent security, sandbox isolation, tool restrictions, budget limits, bridge API, and fail-closed patterns.
billing
Generate subscription billing with metered usage.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
memstack-business-financial-model
Use this skill when the user says 'financial model', 'projections', 'revenue forecast', 'unit economics', 'break-even', 'cash flow', or mentions MRR, churn, CAC, LTV, or runway. Builds monthly projections with scenario modeling. Do NOT use for pricing strategy or invoice generation.
ln-11-plan-reviewer
Reviews an implementation plan against repository evidence before execution; identifies missing decisions and risks. Not for completed-code review.