MediaGo is a cross-platform application for finding and downloading online video streams, including m3u8/HLS media and videos from services such as YouTube and Bilibili. It is for people and automated tools that need to save videos through a desktop app, Docker, browser extension, or HTTP API. The catalogue entries let coding agents operate MediaGo to create downloads and check their progress.
Borrowing it
Nothing to install: this file belongs to mediago-dev/mediago. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mediago-dev/mediago/master/.agents/skills/design-taste-frontend/SKILL.mdgit clone --depth 1 https://github.com/mediago-dev/mediagoWrote 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/mediago-dev/mediago/design-taste-frontend)<a href="https://agentmods.dev/skills/mediago-dev/mediago/design-taste-frontend"><img src="https://agentmods.dev/badge/skills/mediago-dev/mediago/design-taste-frontend/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/mediago-dev/mediago/design-taste-frontend"><img src="https://agentmods.dev/badge/skills/mediago-dev/mediago/design-taste-frontend.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, 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 Prompt Injection · line 272 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Excessive Agency · line 51 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 99 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]
- medium MCP Rug Pull · line 1006 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]
- medium MCP Rug Pull · line 1007 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 | $0.00061 | $0.21912 |
| Opus 5 | $0.00030 | $0.10956 |
| Sonnet 5 | $0.00012 | $0.04382 |
| Haiku 4.5 | $0.00006 | $0.02191 |
Grade A, and why
design-taste-frontend 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 11d 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.
This is a copy
100% identical to design-taste-frontend — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tasteskill: Anti-Slop Frontend Skill
Landing pages, portfolios, and redesigns. Not dashboards, not data tables, not multi-step product UI. Every rule below is contextual. None of it fires automatically. First read the brief, then pull only what fits.
0. BRIEF INFERENCE (Read the Room Before Anything Else)
Before touching code or tweaking dials, infer what the user actually wants. Most LLM design output is bad because the model jumps to a default aesthetic instead of reading the room.
0.A Read these signals first
- Page kind - landing (SaaS / consumer / agency / event), portfolio (dev / designer / creative studio), redesign (preserve vs overhaul), editorial / blog.
- Vibe words the user used - "minimalist", "calm", "Linear-style", "Awwwards", "brutalist", "premium consumer", "Apple-y", "playful", "serious B2B", "editorial", "agency-y", "glassy", "dark tech".
- Reference signals - URLs they linked, screenshots they pasted, products they named, brands they're competing with.
- Audience - B2B procurement panel vs. design-conscious consumer vs. recruiter scanning a portfolio. The audience picks the aesthetic, not your taste.
- Brand assets that already exist - logo, color, type, photography. For redesigns, these are starting material, not optional input (see Section 11).
- Quiet constraints - accessibility-first audiences, public-sector, regulated industries, trust-first commerce, kids' products. These constraints OVERRIDE aesthetic preference.
0.B Output a one-line "Design Read" before generating
Before any code, state in one line: "Reading this as: <page kind> for <audience>, with a <vibe> language, leaning toward <design system or aesthetic family>."
Example reads:
- "Reading this as: B2B SaaS landing for technical buyers, with a Linear-style minimalist language, leaning toward Tailwind utilities + Geist + restrained motion."
- "Reading this as: solo designer portfolio for hiring managers, with an editorial / kinetic-type language, leaning toward native CSS + scroll-driven animation + custom typography."
- "Reading this as: redesign of a public-sector service site, with a trust-first language, leaning toward GOV.UK Frontend or USWDS."
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.
- 11d ago First seen · 1,207 lines · 61 tokens per session scan A aa194351b246
design-taste-frontend is a skill published in the GitHub repository mediago-dev/mediago (9,219 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 21,912 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to design-taste-frontend, differing in 0 lines, and is treated as a copy.
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