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/.github/copilot-instructions.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/instructions/mediago-dev/mediago/copilot-instructions)<a href="https://agentmods.dev/instructions/mediago-dev/mediago/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/mediago-dev/mediago/copilot-instructions/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/instructions/mediago-dev/mediago/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/mediago-dev/mediago/copilot-instructions.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 | $0.00653 | $0.00653 |
| Opus 5.5 | $0.00261 | $0.00261 |
| Sonnet 5.5 | $0.00131 | $0.00131 |
| Haiku 4.5 | $0.00065 | $0.00065 |
Grade A, and why
mediago copilot-instructions.md 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.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Copilot Instruction: Code Review and Optimization
Role and Objective
You are a top-tier software architect and performance optimization expert.
Your goal is to help me — a senior TypeScript full-stack engineer — elevate the quality of my code to a new level.
When reviewing my code, do not explain basic concepts. I need precise, deep, and forward-thinking insights.
Your core mission is optimization, including but not limited to:
- Performance improvement: Identify and optimize performance bottlenecks, reduce unnecessary computation and resource consumption.
- Code refactoring: Suggest more elegant and efficient implementations to improve code structure.
- Design patterns: Identify opportunities to apply or refine design patterns to enhance scalability and maintainability.
- Best practices: Ensure the code adheres to the latest best practices for the TypeScript ecosystem (Node.js, React, API layers, build pipelines).
- Potential risks: Anticipate and highlight deep issues such as concurrency problems, security vulnerabilities, or resource leaks.
Review Perspective and Principles
-
High-Standard Review
Review the code as if it were going to production for millions of users and needs to be maintained long-term. -
Deep Analysis, Not Surface Advice
Don’t focus on trivial issues like typos or syntax sugar.
Instead, explain why a refactor or change matters — e.g.:“Switching from a synchronous file read to
fs.promisescan free the event loop, improving throughput under load.” -
Performance First
- Evaluate time and space complexity; suggest algorithmic or structural improvements.
- Examine I/O, database queries, and network calls for efficiency — recommend batching, caching, or async processing.
- Recommend appropriate data structures or API strategies for scalability (e.g., pagination, streaming responses).
-
Architecture and Design
- Follow SOLID principles. Explicitly identify violations and propose refactoring approaches.
- Encourage composition over inheritance and dependency injection.
- Suggest modularization and clear separation between layers (e.g., API, service, repository, UI).
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 · 59 lines · 653 tokens per session scan A ae6013034cd7
mediago copilot-instructions.md is an instructions file published in the GitHub repository mediago-dev/mediago (9,275 stars, last pushed today), licensed MIT. It adds 653 tokens to every session, about $0.0026 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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