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 dasein108/yt-mem-ai --skill ytgit clone --depth 1 https://github.com/dasein108/yt-mem-aiWrote 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/dasein108/yt-mem-ai/yt)<a href="https://agentmods.dev/skills/dasein108/yt-mem-ai/yt"><img src="https://agentmods.dev/badge/skills/dasein108/yt-mem-ai/yt/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/dasein108/yt-mem-ai/yt"><img src="https://agentmods.dev/badge/skills/dasein108/yt-mem-ai/yt.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 | $0.00087 | $0.02179 |
| Opus 5 | $0.00044 | $0.01090 |
| Sonnet 5 | $0.00017 | $0.00436 |
| Haiku 4.5 | $0.00009 | $0.00218 |
Grade A, and why
yt 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 11d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
yt — the yt-mem-ai entry point
Single entry point for driving the yt-mem-ai YouTube pipeline CLI. Every data operation goes through the CLI — never touch the LanceDB store directly.
Prereqs
- Always invoke the CLI as
uvx yt-mem-ai <cmd>— zero-install, cached, always latest. Nothing is installed on PATH: the native plugins ship skills only, so do NOT go hunting for ayt-aibinary, a wrapper script, or a venv. (Only inside a source checkout of this repo may you useuv run yt-ai <cmd>.) - Always single-quote a video/channel URL — YouTube URLs contain
?and&, which the shell treats as glob and job-control metacharacters, so a bare URL fails (zsh:no matches found). Writeuvx yt-mem-ai fetch 'https://www.youtube.com/watch?v=ID', never bare. Same fortranscriptandchannel-list. - Settings live in a global config file, not a
.envyou hand-edit: inspect and change anything withuvx yt-mem-ai config list/uvx yt-mem-ai config set KEY VALUE(see Configure & maintain). Preferconfig setover exporting env vars — eachuvxrun is a fresh process, so an env var only applies to the one command you prefixed it to. - Video lifecycle status:
discovered → transcribed → summarized(live streams get a terminalstreamand skip transcription).
uvx yt-mem-ai <command> [args]
Decide what the user wants, then run
Ingest one video
uvx yt-mem-ai fetch '<url>' # download audio + transcribe + embed + store
uvx yt-mem-ai transcript '<url>' # same pipeline (alias intent)
Discover + batch ingest (subscriptions)
uvx yt-mem-ai discover [--after <DATE>] [--deep] [--min-duration <s>] [--json]
uvx yt-mem-ai fetch-pending [--since <DATE>] [--limit <N>] # ingest 'discovered' videos
Enumerate a channel (does not ingest)
uvx yt-mem-ai channel-list '<url>' [--limit <N>] [--from <DATE>] [--to <DATE>] [--json]
# newest uploads for a channel URL/@handle; feed the URLs to `fetch` to ingest a group.
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.
- 11d ago First seen · 169 lines · 87 tokens per session scan A 6914761ba0a7
yt is a skill published in the GitHub repository dasein108/yt-mem-ai (7 stars, last pushed 18d ago), licensed MIT. It adds 87 tokens to every session and 2,179 once invoked, about $0.0004 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 2026-08-31.
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