Borrowing it
Nothing to install: this file belongs to dasein108/yt-mem-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dasein108/yt-mem-ai/main/AGENTS.mdgit 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/instructions/dasein108/yt-mem-ai/agents-md)<a href="https://agentmods.dev/instructions/dasein108/yt-mem-ai/agents-md"><img src="https://agentmods.dev/badge/instructions/dasein108/yt-mem-ai/agents-md/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/instructions/dasein108/yt-mem-ai/agents-md"><img src="https://agentmods.dev/badge/instructions/dasein108/yt-mem-ai/agents-md.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 | $0.00457 | $0.00457 |
| Opus 5 | $0.00229 | $0.00229 |
| Sonnet 5 | $0.00091 | $0.00091 |
| Haiku 4.5 | $0.00046 | $0.00046 |
Grade A, and why
yt-mem-ai AGENTS.md 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 9d 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.
What it actually says
Repository Instructions — yt-mem-ai (engine)
This repo is the Python engine published to PyPI as yt-mem-ai. The companion
yt-mem-ai-desktop repo depends on it: its React/Electron UI talks to the engine
over the local HTTP API, and its Python backend imports this package and reuses
cli.py's CLI cores.
Surface parity
When adding, changing, or removing a user-facing operation, keep every surface in sync in the same change:
- Core logic in
yt_summary/(single source of truth). - CLI in
yt_summary/cli.py(thinrun_*cores over the same core). - The REST API lives in the
yt-mem-ai-desktoprepo's backend (it imports this package). When you change a CLI core (run_*,open_store) that the API consumes, keep that repo's backend in sync. - Canonical skills in
skills/<name>/SKILL.md(the.claude/skills/<name>symlinks are thin pointers — never duplicate the body). - Tests in
tests/(offline via the injectable seams; no network, no model downloads).
The CLI and API are thin adapters over the same core — do not fork logic into either surface.
The frame command shares supercut.py's download/ffmpeg approach — when you
change the section-download format or the ffmpeg invocation in one, check the other.
Packaging
- Version comes from git tags via
hatch-vcs. Do not hand-edit a version. - Release = push a
v*tag;.github/workflows/publish-pypi.ymlbuilds and publishes via PyPI Trusted Publishing (OIDC, no stored token).
Deferred (phase 2, not in this repo yet)
yt_summary/server.pyMCP server +yt_summary/installer/cross-agent config writer. When added, they become additional surfaces under "Surface parity" above and get their ownyt-mem-ai-mcp/yt-mem-ai-installconsole scripts.
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.
- 9d ago First seen · 40 lines · 457 tokens per session scan A 23af5eefeade
yt-mem-ai AGENTS.md is an instructions file published in the GitHub repository dasein108/yt-mem-ai (7 stars, last pushed 16d ago), licensed MIT. It adds 457 tokens to every session, about $0.0023 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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