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 skills add nobodyohm-web/Thot --skill youtube-contentgit clone --depth 1 https://github.com/nobodyohm-web/ThotWrote 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/nobodyohm-web/thot/youtube-content)<a href="https://agentmods.dev/skills/nobodyohm-web/thot/youtube-content"><img src="https://agentmods.dev/badge/skills/nobodyohm-web/thot/youtube-content/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/skills/nobodyohm-web/thot/youtube-content"><img src="https://agentmods.dev/badge/skills/nobodyohm-web/thot/youtube-content.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 | $0.00013 | $0.00807 |
| Opus 5 | $0.00006 | $0.00404 |
| Sonnet 5 | $0.00003 | $0.00161 |
| Haiku 4.5 | $0.00001 | $0.00081 |
Grade A, and why
youtube-content 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.
This is a copy
100% identical to youtube-content — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Content Tool
When to use
Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video. Transforms transcripts into structured content (chapters, summaries, threads, blog posts).
Extract transcripts from YouTube videos and convert them into useful formats.
Setup
Use uv so the dependency is installed into the same Hermes-managed environment
that runs the helper script:
uv pip install youtube-transcript-api
Helper Script
SKILL_DIR is the directory containing this SKILL.md file. The script accepts any standard YouTube URL format, short links (youtu.be), shorts, embeds, live links, or a raw 11-character video ID.
# JSON output with metadata
uv run python SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID"
# Plain text (good for piping into further processing)
uv run python SKILL_DIR/scripts/fetch_transcript.py "URL" --text-only
# With timestamps
uv run python SKILL_DIR/scripts/fetch_transcript.py "URL" --timestamps
# Specific language with fallback chain
uv run python SKILL_DIR/scripts/fetch_transcript.py "URL" --language tr,en
Output Formats
After fetching the transcript, format it based on what the user asks for:
- Chapters: Group by topic shifts, output timestamped chapter list
- Summary: Concise 5-10 sentence overview of the entire video
- Chapter summaries: Chapters with a short paragraph summary for each
- Thread: Twitter/X thread format — numbered posts, each under 280 chars
- Blog post: Full article with title, sections, and key takeaways
- Quotes: Notable quotes with timestamps
Example — Chapters Output
00:00 Introduction — host opens with the problem statement
03:45 Background — prior work and why existing solutions fall short
12:20 Core method — walkthrough of the proposed approach
24:10 Results — benchmark comparisons and key takeaways
31:55 Q&A — audience questions on scalability and next steps
What ships with it
2 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.
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.
- 5d ago First seen · 84 lines · 13 tokens per session scan A 52349e30ac74
youtube-content is a skill published in the GitHub repository nobodyohm-web/Thot (0 stars, last pushed 14d ago), licensed MIT. It adds 13 tokens to every session and 807 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to youtube-content, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
p5js
Use when users request: p5.js sketches, creative coding, generative art, interactive visualizations, canvas animations, browser-based visual art, data viz, shader effects, or any p5.js project.
audiocraft-audio-generation
AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
hyperframes
Render MP4/WebM videos from HTML compositions.
stable-diffusion
Text-to-image generation, inpainting, and img2img.
draw-your-font
Turn a handwriting photo into an installable TTF font.
heartmula
HeartMuLa: Suno-like song generation from lyrics + tags.