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 yt-agentgit 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-agent)<a href="https://agentmods.dev/skills/dasein108/yt-mem-ai/yt-agent"><img src="https://agentmods.dev/badge/skills/dasein108/yt-mem-ai/yt-agent/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-agent"><img src="https://agentmods.dev/badge/skills/dasein108/yt-mem-ai/yt-agent.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 | $0.00134 | $0.02403 |
| Opus 5 | $0.00067 | $0.01202 |
| Sonnet 5 | $0.00027 | $0.00481 |
| Haiku 4.5 | $0.00013 | $0.00240 |
Grade A, and why
yt-agent 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
yt-agent — summarize / highlights / Q&A / presentation / digest / review
One entry point for turning YouTube into artifacts. All data access goes through
the uvx yt-mem-ai <cmd> CLI (see [[yt]] for the full command
surface) — never touch the LanceDB store directly. Always invoke it exactly that
way: zero-install and cached, so nothing has to be on PATH. Do not go looking
for a yt-ai binary — the native plugins install no package. (Only inside a
source checkout may you use uv run yt-ai <cmd>.) Single-quote every URL —
YouTube URLs contain ?/&, which the shell globs on, so a bare URL fails:
uvx yt-mem-ai fetch 'https://www.youtube.com/watch?v=ID' --captions-only.
Everything is grounded in the transcript; highlight timestamps come from
uvx yt-mem-ai search, never invented. The analysis is done by this agent — no API
key, no OpenRouter.
Pick the scenario
- A — one video (a URL, a bare 11-char
video_id, or "this video" as a follow-up): produce asummarize/highlights/qa/presentationartifact. - B — process latest subscriptions ("catch up", "daily", "new uploads"): discover + fetch + analyze each + write a dated digest.
- C — subscriptions review ("review my subs", "themes lately", "what's been happening"): one cross-video synthesis over a date range.
- D — group (arbitrary set) ("process/review these videos <ids/urls>", "review channel ''", "review from to "): ingest a user-specified set, then per-video analysis + a group synthesis.
Core: analyze one video (used by A and B)
Given a video_id (and a URL if it may not be ingested yet):
- Ensure ingested (idempotent):
uvx yt-mem-ai show <video_id> --json.not foundand you have a URL →uvx yt-mem-ai fetch '<url>' --captions-only. If that printsno captions available: ..., fall back touvx yt-mem-ai fetch '<url>' --whisper(downloads audio + transcribes — slower, always yields a transcript).- Blocked instead?
Sign in to confirm you're not a bot→ runuvx yt-mem-ai config set YT_COOKIES_BROWSER chromeand retry;captions blocked ... IP rate-limited→ cookies won't help, see [[yt]]'s When YouTube blocks a fetch. - Already ingested → instant, no download (
is_seenskips it).
- Reuse if present: if the
show --jsonoutput has a non-nullsummary, reuse it — skip generation unless the user asked for a fresh artifact. - Anchor highlights: for each candidate highlight phrase, run
uvx yt-mem-ai search "<phrase>" --vector -k 3and use theMM:SSfrom a returned line whosevideo_idmatches. Never invent timestamps. - Produce (you, the model — no API):
summary_md(2–4 sentence exec summary- key bullets),
highlightsJSON[{"start_s": <seconds>, "label": "..."}](3–8, seconds from step 3),qaJSON[{"q": "...", "a": "..."}](3–6).
- key bullets),
- Persist:
uvx yt-mem-ai save-summary <video_id> "<summary_md>" --highlights '<json>' --qa '<json>'.
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 · 154 lines · 0 tokens per session scan A 4546e702d5e7
yt-agent is a skill published in the GitHub repository dasein108/yt-mem-ai (7 stars, last pushed 19d ago), licensed MIT. It adds 134 tokens to every session and 2,403 once invoked, about $0.0007 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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