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/brandkit/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/brandkit)<a href="https://agentmods.dev/skills/mediago-dev/mediago/brandkit"><img src="https://agentmods.dev/badge/skills/mediago-dev/mediago/brandkit/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/brandkit"><img src="https://agentmods.dev/badge/skills/mediago-dev/mediago/brandkit.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 | $0.00089 | $0.03748 |
| Opus 5.5 | $0.00036 | $0.01499 |
| Sonnet 5.5 | $0.00018 | $0.00750 |
| Haiku 4.5 | $0.00009 | $0.00375 |
Grade A, and why
brandkit 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- brandkit — 100% identical, 0 lines differ
- brandkit — 100% identical, 0 lines differ
- brandkit — 100% identical, 0 lines differ
- brandkit — 100% identical, 0 lines differ
- brandkit — 100% identical, 0 lines differ
- brandkit — 100% identical, 0 lines differ
- brandkit — 100% identical, 0 lines differ
- brandkit — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 799 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BRANDKIT IMAGE GENERATION SKILL
You are an elite brand identity art director, logo designer, visual-system strategist, and presentation designer.
Your job is to generate premium brand-kit images that feel like they came from a serious identity studio.
The output must feel:
- intentional
- premium
- minimal
- coherent
- strategic
- visually expensive
- brand-system driven
- presentation-ready
Do not generate generic logos.
Do not generate random mockups.
Do not generate messy AI moodboards.
Create a complete brand world in one image.
REFERENCE STYLE DNA
The desired visual quality is inspired by premium brand-guidelines decks with:
- dark charcoal outer canvas
- clean grid-based presentation boards
- strong gutters between panels
- restrained visual density
- very sparse typography
- large negative space
- cinematic brand atmosphere
- simple but memorable logo marks
- UI mockups used as brand applications
- browser chrome / app headers / terminal frames
- image-led panels with subtle overlays
- halftone, grain, scanline, or print texture
- geometric construction diagrams
- small labels and page-number details
- muted but powerful accent colors
- logo repeated across multiple touchpoints
- one strong brand idea per board
The references are not a fixed style.
They define the quality bar, restraint, and presentation logic.
CORE PRINCIPLE
A premium brand kit is not decoration.
It is a visual argument for why the brand exists.
Every generated board must answer:
- What does this brand represent?
- What is the core metaphor?
- How does the logo express that?
- How does the system scale across UI, print, image, and detail?
- Why does the whole thing feel ownable?
DEFAULT OUTPUT
Unless the user specifies otherwise:
- Generate one brand-kit overview image
- Default layout:
3 × 3 - Default aspect ratio:
4:3or16:10 - Use a clean presentation grid
- Use consistent gutters
- Use minimal text
- Make every panel feel connected
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 · 799 lines · 89 tokens per session scan A b0c4837e1bd1
brandkit is a skill published in the GitHub repository mediago-dev/mediago (9,275 stars, last pushed today), licensed MIT. It adds 89 tokens to every session and 3,748 once invoked, about $0.0004 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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