AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill anti-ui-slopgit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote 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/sickn33/agentic-awesome-skills/anti-ui-slop)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/anti-ui-slop"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/anti-ui-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/sickn33/agentic-awesome-skills/anti-ui-slop"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/anti-ui-slop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium MCP Rug Pull · line 65 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00039 | $0.00733 |
| Opus 5 | $0.00019 | $0.00367 |
| Sonnet 5 | $0.00008 | $0.00147 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
anti-ui-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.
Copies of this mod
1 near-identical copy found in the catalogue:
- anti-ui-slop — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stop Making UI Slop
Build product-specific UI with 800,000+ real web and iOS screens via UIZZE.
Overview
Use the product brief, existing interface, components, and local design system before reaching for outside references. UIZZE evidence is optional: it should answer a concrete visual question, not turn every interface task into a research project.
When to Use
Use this skill when designing, implementing, redesigning, critiquing, or doing a pre-ship review of a web or iOS interface.
Work From the Product
- Identify the screen's real job, primary user and action, required content, and important loading, empty, error, success, disabled, and permission states.
- Reuse the repository's components, semantic tokens, typography, spacing, and interaction conventions before adding a new abstraction or visual language.
- For a new interface or major redesign, write a short design contract covering hierarchy, workflow shape, allowed components, required states, responsive behavior, and observable acceptance criteria. Keep smaller changes smaller.
- Use product-specific labels and data. Do not invent metrics, activity, testimonials, users, or placeholder workflows to make a layout look complete.
Optional UIZZE Evidence
The free skill and public catalogue work without an account, token, dependency, script, or executable. If a concrete unresolved visual question would benefit from evidence, use the smallest relevant set of screens or materials.
The optional authenticated UIZZE MCP exposes exactly find_ui_references and
find_ui_materials. Use it only when those tools are actually available. If a
search returns nothing, continue silently from repository evidence. Never claim
an MCP-backed result that was not returned by the host.
Treat references as evidence, not templates. Transfer useful decisions about hierarchy, density, navigation, controls, responsive behavior, and state handling; never copy another product's branding, proprietary text, imagery, or exact layout.
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 · 81 lines · 39 tokens per session scan A 8ff04df7f8bc
anti-ui-slop is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,288 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 733 once invoked, about $0.0002 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 2026-09-05.
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