Borrowing it
Nothing to install: this file belongs to SihyunAdventure/dubyduby. 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/SihyunAdventure/dubyduby/main/AGENTS.mdgit clone --depth 1 https://github.com/SihyunAdventure/dubydubyWrote 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/sihyunadventure/dubyduby/agents-md)<a href="https://agentmods.dev/instructions/sihyunadventure/dubyduby/agents-md"><img src="https://agentmods.dev/badge/instructions/sihyunadventure/dubyduby/agents-md/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/sihyunadventure/dubyduby/agents-md"><img src="https://agentmods.dev/badge/instructions/sihyunadventure/dubyduby/agents-md.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 | $0.02807 | $0.02807 |
| Opus 5 | $0.01404 | $0.01404 |
| Sonnet 5 | $0.00561 | $0.00561 |
| Haiku 4.5 | $0.00281 | $0.00281 |
Grade A, and why
dubyduby AGENTS.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 10d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dubyduby — Agent instructions
You're an AI agent (Claude, Codex, Cursor, etc.) helping a user dub YouTube videos into Korean.
Trigger
User says any of:
- "이 URL dub해줘"
- "dub this URL"
- "dub this video"
- "이 영상 한국어로"
- Pastes a YouTube URL with intent to translate
Two-phase workflow
bash scripts/dub.sh <URL> orchestrates everything. It runs in two phases with a pause between them where you (the agent) write the translation.
Phase 1 — download + transcribe (automatic)
bash scripts/dub.sh https://youtu.be/EXAMPLE
This:
- Downloads video + audio via yt-dlp →
output/<video_id>/1_source/{video.mp4, audio.mp3} - Calls Soniox STT batch →
output/<video_id>/2_transcript/{tokens.json, transcript.md} - Pauses — prints instructions and exits.
If user wants only the first N seconds: bash scripts/dub.sh <URL> 120 (cuts at 120s).
Phase 2 — agent writes translation
Before translating: read glossary.json at repo root. It maps known STT misreads → canonical brand/term names, plus Korean phonetic spellings used in TTS. Apply during transcript cleanup before writing sentences.json.
Read output/<video_id>/2_transcript/transcript.md (full EN text from Soniox).
Write output/<video_id>/3_translation/sentences.json as an array of {en, ko}:
[
{ "en": "Hey everybody,", "ko": "여러분 안녕하세요." },
{ "en": "Opus 4.7 just dropped a few minutes ago,", "ko": "Opus 사 점 칠이 방금 출시됐는데요." }
]
Then re-run the same orchestrator command:
bash scripts/dub.sh https://youtu.be/EXAMPLE
This time it detects sentences.json and proceeds: match_timing → synthesize → finalize → output/<video_id>/6_final/dubbed_video.mp4.
Glossary — STT misread fix + Korean phonetic
glossary.json (repo root) is the source of truth for brand names and recurring terms. Each entry:
{
"canonical": "Claude Code",
"stt_misreads": ["Cloth Code", "Cloud Code"],
"korean_phonetic": "클로드 코드",
"category": "ai-product"
}
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.
- 10d ago First seen · 249 lines · 2,807 tokens per session scan A 27ca812a3972
dubyduby AGENTS.md is an instructions file published in the GitHub repository SihyunAdventure/dubyduby (19 stars, last pushed 3mo ago), licensed MIT. It adds 2,807 tokens to every session, about $0.0140 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 2026-08-30.
Other instructions, from other repositories
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AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
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AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
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spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
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