video-to-notebook: Instructions file for Codex

AGENTS.md

video-to-notebook AGENTS.md is an instructions file for Codex, OpenCode from LinZhuoChen/video-to-notebook. It costs 3,394 tokens per session, scanned C, original, MIT.

An instruction guide for coding agents working on a Python command-line tool that turns video playlists into illustrated, textbook-like websites. It explains the repository and its agent-driven workflow.

In plain words
What is it for?
Use it when an agent needs to crawl YouTube or Bilibili playlists, label transcripts, group concepts, generate learning content, or build the resulting Astro website.
Why use it?
It gives an agent the project context and the steps needed to run or continue the workflow without guessing how the codebase is organised.

Instructions file for CodexOpenCode

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

This is LinZhuoChen/video-to-notebook's own configuration. It tells Codex and OpenCode how to work on video-to-notebook 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 video-to-notebook configures →

Reuse

Borrowing it

Nothing to install: this file belongs to LinZhuoChen/video-to-notebook. 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/LinZhuoChen/video-to-notebook/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/LinZhuoChen/video-to-notebook

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for video-to-notebook AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/linzhuochen/video-to-notebook/agents-md.svg)](https://agentmods.dev/instructions/linzhuochen/video-to-notebook/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/linzhuochen/video-to-notebook/agents-md"><img src="https://agentmods.dev/badge/instructions/linzhuochen/video-to-notebook/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 3,394 This file is loaded in full into every session.
When invoked 3,394 The same file — it is already loaded in full.
Security scan C 1 finding. 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 $0.03394 $0.03394
Opus 5 $0.01697 $0.01697
Sonnet 5 $0.00679 $0.00679
Haiku 4.5 $0.00339 $0.00339

Measured 7d ago against content hash f960b2b51152, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade C, and why

video-to-notebook AGENTS.md scanned grade C with 1 finding 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 7d 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf .video-to-notebook/textbook/*.html .video-to-notebook/concepts/*.html
AGENTS.md · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agents guide

This file orients AI coding agents (OpenAI Codex CLI, Cursor, Continue, Aider, …) to the video-to-notebook codebase. Claude Code users: see skills/video-to-notebook/SKILL.md for the equivalent skill-format walkthrough — the two files cover the same ground.

What this repo is

A Python CLI + Astro static-site generator that:

  1. Crawls YouTube + Bilibili playlists with yt-dlp → SQLite.
  2. Tags transcript chunks with concept labels via Claude (or any agent).
  3. Clusters proposed tags into a unified ontology.
  4. Synthesizes a beginner-friendly textbook (one HTML chapter per concept group).
  5. Explains each concept as a rich illustrated encyclopedia entry.
  6. Builds the lot into a static Astro site you can host on GitHub Pages.

Agent-driven workflow (default in v2.3+)

Every LLM stage (tag, cluster, curriculum, synthesize, explain) runs in-session by default — no ANTHROPIC_API_KEY required. The CLI writes a JSON prompts envelope to <state_dir>/prompts/<step>.json and exits. You read the envelope, reason, write a decisions JSON to the sibling .decisions.json path, then re-invoke the same command with --apply. The CLI applies your decisions to SQLite.

tag and cluster also expose an opt-in --use-api flag that drives the Anthropic SDK directly (if you have a key). curriculum, synthesize, explain are in-session-only — they have no API path.

The protocol is agent-agnostic. Schemas, conventions, idempotency guarantees, error semantics all live in docs/AGENT_PROTOCOL.md. Read that file before driving the pipeline for the first time.

Quick start for Codex / any agent

mkdir my-study-site && cd my-study-site
video-to-notebook init
video-to-notebook crawl "<youtube-or-bilibili-url>" --name <slug>
# Repeat crawl for each course

# Then, for each LLM stage:
video-to-notebook <stage> [args]
# CLI writes <state_dir>/prompts/<step>.json and prints a 3-line stderr hint.
# You read the envelope, reason, write <state_dir>/prompts/<step>.decisions.json.
video-to-notebook <stage> [args] --apply

Read the full file on GitHub · 219 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. 7d ago First seen · 219 lines · 3,394 tokens per session scan C f960b2b51152

Subscribe to this mod's changes

video-to-notebook AGENTS.md is an instructions file published in the GitHub repository LinZhuoChen/video-to-notebook (21 stars, last pushed 3mo ago), licensed MIT. It adds 3,394 tokens to every session, about $0.0170 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other instructions, from other repositories

next.js AGENTS.md

AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

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).

microsoft/vscode · 5,001 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,469 tokens

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.

github/spec-kit · 7,104 tokens