Borrowing it
Nothing to install: this file belongs to zlatkoc/youtube-summarize. 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/zlatkoc/youtube-summarize/main/CLAUDE.mdgit clone --depth 1 https://github.com/zlatkoc/youtube-summarizeWrote 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/zlatkoc/youtube-summarize/claude-md)<a href="https://agentmods.dev/instructions/zlatkoc/youtube-summarize/claude-md"><img src="https://agentmods.dev/badge/instructions/zlatkoc/youtube-summarize/claude-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/zlatkoc/youtube-summarize/claude-md"><img src="https://agentmods.dev/badge/instructions/zlatkoc/youtube-summarize/claude-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 | $0.00922 | $0.00922 |
| Opus 5 | $0.00461 | $0.00461 |
| Sonnet 5 | $0.00184 | $0.00184 |
| Haiku 4.5 | $0.00092 | $0.00092 |
Grade A, and why
youtube-summarize CLAUDE.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 8d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
MCP (Model Context Protocol) server that retrieves YouTube video transcripts and optionally summarizes them. It uses the youtube_transcript_api Python library directly and exposes its functionality as MCP tools.
Key Commands
# Install dependencies
uv sync
# Run the MCP server (stdio transport)
uv run mcp run main.py
# Run the MCP dev inspector (web UI for testing)
uv run mcp dev main.py
Architecture
- Entry point:
main.py— defines the MCPServer instance and all MCP tools - Transport: stdio-based MCP server (launched via
mcp run) - Library:
youtube_transcript_api— used directly as a Python library (not as a subprocess/CLI)
MCP Tools
get_transcript— fetch a YouTube video's transcript in a specified format (json, pretty, text, webvtt, srt), with optional language selectionlist_transcripts— list available transcript languages for a videosummarize_transcript— fetch transcript and return it with summarization instructions for the LLM client to act onsearch_videos— search YouTube via yt-dlp (no ads/recommendations/personalization), with sort (relevance/date/views/rating) and upload-date/duration filters encoded as YouTube'sspprotobuf parameterget_video_metadata— fetch full metadata (title, description, channel, upload date, duration, views, chapters, tags, etc.) for a video via yt-dlplist_playlist_videos— list a playlist's videos (titles, IDs, channels, durations, views) via yt-dlp's flat extraction, with sorting and a limit
Design Decisions
- Uses
youtube_transcript_apias a Python library directly (structured data, proper exceptions, no subprocess overhead) - Formatter classes from
youtube_transcript_api.formattershandle output formatting (JSON, Text, SRT, WebVTT, PrettyPrint) - The summarization tool returns the transcript with a prompt/instruction for the LLM to summarize, rather than calling an external LLM API itself (the MCP client/LLM handles summarization)
- Video IDs are extracted from full YouTube URLs when provided (supports
youtube.com/watch?v=,youtu.be/,youtube.com/embed/,youtube.com/shorts/, and bare 11-char IDs) - Tools return error strings rather than raising exceptions — the LLM client can act on them
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.
- 8d ago First seen · 83 lines · 922 tokens per session scan A 1c21e960745f
youtube-summarize CLAUDE.md is an instructions file published in the GitHub repository zlatkoc/youtube-summarize (6 stars, last pushed 17d ago), licensed MIT. It adds 922 tokens to every session, about $0.0046 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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