dubyduby: Instructions file for Codex

AGENTS.md

dubyduby AGENTS.md is an instructions file for Codex, OpenCode from SihyunAdventure/dubyduby. It costs 2,807 tokens per session, scanned A, original, MIT.

Instructions for dubbing YouTube videos into Korean through a two-stage process: downloading and transcribing the video, then writing and applying the translation. A transcript is the written version of spoken audio.

In plain words
What is it for?
Use it when a user asks to dub a YouTube video into Korean, including downloading its media, creating a transcript, applying the glossary, and preparing translated sentences.
Why use it?
They make the workflow repeatable and ensure terminology is cleaned up consistently before the translated speech is produced.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions Claude Code; mentions Codex.

This is SihyunAdventure/dubyduby's own configuration. It tells Codex and OpenCode how to work on dubyduby itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything dubyduby configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/SihyunAdventure/dubyduby/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/SihyunAdventure/dubyduby

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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Per session 2,807 This file is loaded in full into every session.
When invoked 2,807 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash 27ca812a3972, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

AGENTS.md · 249 lines

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:

  1. Downloads video + audio via yt-dlp → output/<video_id>/1_source/{video.mp4, audio.mp3}
  2. Calls Soniox STT batch → output/<video_id>/2_transcript/{tokens.json, transcript.md}
  3. 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"
}

Read the full file on GitHub · 249 lines

Changes

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.

  1. 10d ago First seen · 249 lines · 2,807 tokens per session scan A 27ca812a3972

Subscribe to this mod's changes

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.

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