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.
npx agentmods add skills/nicepkg/ai-workflow/youtube-to-markdownnpx skills add nicepkg/ai-workflow --skill youtube-to-markdowngit clone --depth 1 https://github.com/nicepkg/ai-workflowWrote 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/skills/nicepkg/ai-workflow/youtube-to-markdown)<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/youtube-to-markdown"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/youtube-to-markdown.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.01757 |
| Opus 5 | $0.00017 | $0.00879 |
| Sonnet 5 | $0.00007 | $0.00351 |
| Haiku 4.5 | $0.00003 | $0.00176 |
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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- youtube-to-markdown — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube to Markdown
Multiple videos: Process one video at a time, sequentially. Do not run parallel extractions. Execute all steps sequentially without asking for user approval. Use TodoWrite to track progress.
Step 0: Check extracted before
python3 ./check_existing.py "<YOUTUBE_URL>" "<output_directory>"
Integrity check:
- If
summary_valid: false: Show issues to user, ask "Tiedosto epätäydellinen: [issues]. Prosessoidaanko uudelleen?" If yes, continue to Step 1. - If
transcript_valid: false: Ask user, if yes re-run Steps 2-3, 7-9. - If
comments_valid: false: Ask user, if yes re-run comment analysis.
If returns exists: true AND all valid fields are true: Read and follow UPDATE_MODE.md for update workflow.
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
IMPORTANT: If you ask which language transcript to extract then do not translate that language to english and require that subagent do not translate either. Only if the user requests another language that the original then translate.
Step 2: Extract transcript
Primary method (if transcript available)
If video language is en, proceed directly. If non-English, ask user which language to download.
python3 extract_transcript.py "<YOUTUBE_URL>" "<output_directory>" "<LANG_CODE>"
Creates: youtube_{VIDEO_ID}_transcript.vtt
IMPORTANT: All file output must be in the same language as discovered in Step 2. If language is not English, explicitly instruct all subagents to preserve the original language.
The download may fail if a video is private, age-restricted, or geo-blocked.
Fallback (only if transcript unavailable)
Ask user: "No transcript available. Proceed with Whisper transcription?
- Mac/Apple Silicon: Uses MLX Whisper if installed (faster, see SETUP_MLX_WHISPER.md)
- All platforms: Falls back to OpenAI Whisper (requires: brew install openai-whisper OR pip3 install openai-whisper)"
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- apply_paragraph_breaks.py 6.7 KB runs code
- check_existing.py 7.5 KB runs code
- deduplicate_vtt.py 4.1 KB runs code
- extract_data.py 9.9 KB runs code
- extract_transcript_whisper.py 9.1 KB runs code
- extract_transcript.py 7.4 KB runs code
- file_ops.py 3.5 KB runs code
- finalize.py 9.0 KB runs code
- LICENSE 1.1 KB
- shared_types.py 7.5 KB runs code
- summary_formats.md 888 B
- template_transcript.md 65 B
- template.md 44 B
- UPDATE_MODE.md 2.2 KB
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.
- 2d ago First seen · 216 lines · 35 tokens per session scan A 22ab5ecd5b77
youtube-to-markdown is a skill published in the GitHub repository nicepkg/ai-workflow (282 stars, last pushed 7mo ago), licensed MIT. It adds 35 tokens to every session and 1,757 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 2026-09-03.
Other skills, from other repositories
pptx
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当用户需要对PDF文件进行任何操作时，请使用此技能。包括从 PDF 中读取或提取文本/表格、合并多个 PDF、拆分 PDF、旋转页面、添加水印、创建新PDF、填写PDF表单、加密/解密 PDF、提取图片，以及对扫描版 PDF 进行 OCR 使其可搜索。如果用户提到 .pdf 文件或要求生成 PDF，请使用此技能。.
nano-pdf
Edits PDF files using natural-language instructions via the nano-pdf CLI. Supports modifying text, changing titles, fixing typos, and updating content on specific pages. Use when the user wants to edit a PDF, modify PDF content, update PDF text, fix a typo in a PDF, change a PDF title, or rewrite part of a PDF page.
pdf-toolkit
Structured .pdf operations: extract text/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract…
hive.pdf
Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF…
sn-ppt-entry
Entry point for PPT generation. Asks the user to choose a mode (fast, standard, or creative), then collects role / audience / scene / pagecount as needed. For standard mode, also asks how images should be sourced (AI generation, web search, or none), whether charts should use AI-generated infographics or ECharts, and…