youtube-to-markdown

youtube-to-markdown is a skill for Claude Code, Codex from vre/flow-state. It costs 35 tokens per session (1,218 once invoked), scanned A, original, MIT.

A tool for turning a YouTube video into structured Markdown files. It can collect video details, descriptions, chapters, subtitles, and comments, with Whisper transcription as a fallback when subtitles are unavailable.

In plain words
What is it for?
Use it to fetch video metadata, descriptions, chapters, transcripts, and comments into an output directory.
Why use it?
It saves you from manually copying video information and speech into notes. The output keeps the extracted material organised for later use.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/vre/flow-state/no-polish
Any agent
npx skills add vre/flow-state --skill no-polish
Clone the repo
git clone --depth 1 https://github.com/vre/flow-state

Made for: Claude Code, Codex.

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 youtube-to-markdown

README.md
[![agentmods](https://agentmods.dev/badge/skills/vre/flow-state/no-polish.svg)](https://agentmods.dev/skills/vre/flow-state/no-polish)
Your own site
<a href="https://agentmods.dev/skills/vre/flow-state/no-polish"><img src="https://agentmods.dev/badge/skills/vre/flow-state/no-polish.svg" alt="Measured on agentmods" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,218 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00035 $0.01218
Opus 5 $0.00017 $0.00609
Sonnet 5 $0.00007 $0.00244
Haiku 4.5 $0.00003 $0.00122

Measured 5d ago against content hash dfc6a33ab046, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

youtube-to-markdown 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 5d 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.

docs/youtube-to-markdown/research/2026.01.14 token usage test/code/no-polish/SKILL.md · 177 lines

How it starts

The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.

YouTube to Markdown (No-Polish Variant)

Test variant: Removes transcript polish steps (4, 7, 8). Keeps separate summary steps (5, 6) and comment steps (10a, 10b).

Execute all steps sequentially without asking for user approval.

Step 0: Check extracted before

python3 ./check_existing.py "<YOUTUBE_URL>" "<output_directory>"

If returns exists: true: Skip to Step 10 (comments).

Step 1: Extract data (metadata, description, chapters)

python3 extract_data.py "<YOUTUBE_URL>" "<output_directory>"

Creates: youtube_{VIDEO_ID}metadata.md, youtube{VIDEO_ID}description.md, youtube{VIDEO_ID}_chapters.json

Step 2: Extract transcript

If video language is en, proceed directly. If non-English, ask user which language.

python3 extract_transcript.py "<YOUTUBE_URL>" "<output_directory>" "<LANG_CODE>"

Creates: youtube_{VIDEO_ID}_transcript.vtt

Fallback (only if transcript unavailable)

Ask user: "No transcript available. Proceed with Whisper transcription?"

python3 extract_transcript_whisper.py "<YOUTUBE_URL>" "<output_directory>"

Step 3: Deduplicate transcript

Set BASE_NAME from Step 1 output (youtube_{VIDEO_ID})

python3 ./deduplicate_vtt.py "<output_directory>/${BASE_NAME}_transcript.vtt" "<output_directory>/${BASE_NAME}_transcript_dedup.md" "<output_directory>/${BASE_NAME}_transcript_no_timestamps.txt"

Copy dedup as final transcript (no polish steps):

cp "<output_directory>/${BASE_NAME}_transcript_dedup.md" "<output_directory>/${BASE_NAME}_transcript.md"

Step 5: Summarize transcript

task_tool:

  • subagent_type: "general-purpose"
  • model: "sonnet"
  • prompt:
INPUT: <output_directory>/${BASE_NAME}_transcript_no_timestamps.txt
OUTPUT: <output_directory>/${BASE_NAME}_summary.md
FORMATS: ./summary_formats.md

1. Classify content type:
   - TIPS: gear reviews, rankings, "X ways to...", practical advice lists
   - INTERVIEW: podcasts, conversations, Q&A, multiple perspectives
   - EDUCATIONAL: concept explanations, analysis, "how X works"
   - TUTORIAL: step-by-step instructions, coding, recipes

2. Analyze content structure:
   - Identify meaningful content units (topic shifts, argument structure, narrative breaks)
   - If single continuous topic, omit content unit headers
   - Skip ads, sponsors, self-promotion

3. Read FORMATS file and use format for detected content type. Target <10% of transcript bytes.

ACTION REQUIRED: Use the Write tool NOW to save output to OUTPUT file.

Read the full file on GitHub · 177 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. 5d ago First seen · 177 lines · 35 tokens per session scan A dfc6a33ab046

Subscribe to this mod's changes

youtube-to-markdown is a skill published in the GitHub repository vre/flow-state (12 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 1,218 once invoked, about $0.0002 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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