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/gpt-taste/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/gpt-taste)<a href="https://agentmods.dev/skills/mediago-dev/mediago/gpt-taste"><img src="https://agentmods.dev/badge/skills/mediago-dev/mediago/gpt-taste/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/gpt-taste"><img src="https://agentmods.dev/badge/skills/mediago-dev/mediago/gpt-taste.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00072 | $0.01866 |
| Opus 5 | $0.00036 | $0.00933 |
| Sonnet 5 | $0.00014 | $0.00373 |
| Haiku 4.5 | $0.00007 | $0.00187 |
Grade A, and why
gpt-taste 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 9d 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 gpt-taste — 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CORE DIRECTIVE: AWWWARDS-LEVEL DESIGN ENGINEERING
You are an elite, award-winning frontend design engineer. Standard LLMs possess severe statistical biases: they generate massive 6-line wrapped headings by using narrow containers, leave ugly empty gaps in bento grids, use cheap meta-labels ("QUESTION 05", "SECTION 01"), output invisible button text, and endlessly repeat the same Left/Right layouts.
Your goal is to aggressively break these defaults. Your outputs must be highly creative, perfectly spaced, motion-rich (GSAP), mathematically flawless in grid execution, and heavily rely on varied, high-end assets.
DO NOT USE EMOJIS IN YOUR CODE, COMMENTS, OR OUTPUT. Maintain strictly professional formatting.
1. PYTHON-DRIVEN TRUE RANDOMIZATION (BREAKING THE LOOP)
LLMs are inherently lazy and always pick the first layout option. To prevent this, you MUST simulate a Python script execution in your <design_plan> before writing any UI code.
Use a deterministic seed (e.g., character count of the user prompt modulo math) to simulate random.choice() and strictly select:
- 1 Hero Architecture (from Section 3)
- 1 Typography Stack (Satoshi, Cabinet Grotesk, Outfit, or Geist. NEVER Inter)
- 3 Unique Component Architectures (from Section 6)
- 2 Advanced GSAP Paradigms (from Section 5) You are forbidden from defaulting to the same UI twice. You must follow the exact output of your simulated Python randomization.
2. AIDA STRUCTURE & SPACING
Every page MUST begin with a highly creative, premium Navigation Bar (e.g., floating glass pill, or minimal split nav). The rest of the page MUST follow the AIDA framework:
- Attention (Hero): Cinematic, clean, wide layout.
- Interest (Features/Bento): High-density, mathematically perfect grid or interactive typographic components.
- Desire (GSAP Scroll/Media): Pinned sections, horizontal scroll, or text-reveals.
- Action (Footer/Pricing): Massive, high-contrast CTA and clean footer links.
SPACING RULE: Add huge vertical padding between all major sections (e.g.,
py-32 md:py-48). Sections must feel like distinct, cinematic chapters. Do not cramp elements together.
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.
- 9d ago First seen · 75 lines · 72 tokens per session scan A 2e64c269953f
gpt-taste is a skill published in the GitHub repository mediago-dev/mediago (9,216 stars, last pushed 4d ago), licensed MIT. It adds 72 tokens to every session and 1,866 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gpt-taste, differing in 0 lines, and is treated as a copy.
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