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 fredoliveira/agents --skill youtube-to-atomsgit clone --depth 1 https://github.com/fredoliveira/agentsWrote 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/fredoliveira/agents/youtube-to-atoms)<a href="https://agentmods.dev/skills/fredoliveira/agents/youtube-to-atoms"><img src="https://agentmods.dev/badge/skills/fredoliveira/agents/youtube-to-atoms/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/fredoliveira/agents/youtube-to-atoms"><img src="https://agentmods.dev/badge/skills/fredoliveira/agents/youtube-to-atoms.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 | $0.00050 | $0.01038 |
| Opus 5.5 | $0.00020 | $0.00415 |
| Sonnet 5.5 | $0.00010 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
youtube-to-atoms 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube → atomic notes
Purpose
Turn one YouTube video into a source note plus a set of atomic notes in an Obsidian vault. The unit of extraction is a solid, transferable idea or concept — not a summary.
The note style (what an atomic note is, frontmatter, linking, a worked example) is fully codified in the bundled reference.md — this skill is self-contained and does not depend on any particular vault existing beforehand.
Configuration
The vault root is the YT_VAULT environment variable, defaulting to
~/Documents/obsidian/yt. The skill reads/writes three subfolders (created
automatically):
$YT_VAULT/Transcripts/— fetched transcript (provenance)$YT_VAULT/Sources/— one source note per video$YT_VAULT/Atoms/— the atomic notes
To target a different vault, set YT_VAULT before invoking (e.g.
export YT_VAULT=~/notes/myvault).
Instructions
1. Fetch the transcript
Run the bundled script with the URL the user gave you:
python3 "${CLAUDE_SKILL_DIR}/fetch_transcript.py" "<youtube url>"
It prints the absolute path of the transcript markdown file (or an ERROR: line
if the video has no English captions, or if YouTube throttled the request —
relay that to the user and stop). If a transcript for that video already exists,
it prints the existing path.
Requires
yt-dlp(python3 -m pip install yt-dlp). Auto-generated captions are used when no human captions exist — they extract fine.
2. Read the transcript IN FULL
Read the printed file completely (page through with offset/limit if it's long —
many videos run for hours). Do not extract from a partial read. The
frontmatter holds url, channel, duration; body paragraphs are prefixed
with **[MM:SS]** timestamps.
3. Learn the style, then extract
Read reference.md (in this skill's directory) — it is the authoritative guide for note shape, linking, and the worked example. Then extract atomic notes from the transcript following it. The essentials:
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.
- 1mo ago First seen · 104 lines · 50 tokens per session scan A 824cbc459cd5
youtube-to-atoms is a skill published in the GitHub repository fredoliveira/agents (25 stars, last pushed 4mo ago), licensed MIT. It adds 50 tokens to every session and 1,038 once invoked, about $0.0002 per session on Opus 5.5. 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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