OK. Now we need a tool that can download (using yt-dlp) and transcribe (using...

Let me explore the codebase to understand the existing tools structure and any related patterns.

That gives great context. Let me explore the existing tools structure and the preparation system so I can plan this well.

Good, I have a solid understanding of both the tools structure and the preparation system. Let me now get a plan agent to design the implementation.

Great, I have a thorough design. Let me ask a key scoping question before finalizing the plan.

Both it is. Let me read the key files I need to understand for the preparation system integration.

Now I have a complete understanding. Let me write the plan.

The plan covers three parts:

1. Transcription tool (tools/transcribe/) — general-purpose yt-dlp + WhisperX pipeline with clean text output
2. Preparation system changes — minimal, backwards-compatible additions: optional contentPath and contentLabel fields so preparations can reference film transcripts instead of just readings
3. AlphaGo preparation content — topics around intelligence, creativity, human response to AI, and learning vs. programming

The preparation topics I drafted are based on your description ("themes of intelligence and creativity"). You can refine those during implementation — the content file is easy to iterate on.

Good idea. Let me check how the embedding/RAG system works so I can add that to the plan.

Great catch. The RAG system already has everything needed — it discovers .mdx files from the content directories and chunks them automatically. The simplest approach is to store the transcript as an MDX file in the readings directory, which means:

• Zero changes to the embedding/RAG pipeline (automatic discovery)
• Zero changes to the preparation system types (use existing readingSlug/readingSemester)
• The TA chatbot immediately has access to the transcript

Let me simplify the plan significantly.

The user wants this to be part of a broader content ingestion system. Let me update the plan to reflect that vision while keeping the immediate scope practical.

Good. The plan now captures the broader vision (general content ingestion pipeline) while keeping the immediate scope to YouTube transcription. The pattern — fetch, convert to text, store as MDX content for RAG — will naturally extend to URLs, PDFs, and other sources later.

The plan has three parts:

1. Transcription tool — tools/transcribe/, yt-dlp + WhisperX pipeline
2. Fix prompt builders — remove hardcoded McLuhan references from preparation-evaluator.ts
3. AlphaGo content — transcript as MDX in readings (auto-RAG'd) + preparation chat content file

That's an important distinction. Let me check how readings are currently surfaced on the website to understand the implications.

Good questions. Let me check how sidenotes are currently handled during RAG embedding, since that's a concrete question I can answer now.

Interesting findings on sidenotes:

Current state: The embedding tool passes raw MDX content directly to the chunker. Sidenotes like <Sidenote id="..." label="...">content</Sidenote> end up in RAG chunks as raw markup. The LLM can probably interpret them, but it's noisy. The reading-loader (used for preparation chat context) does strip them via regex, but the RAG pipeline does not.

The broader vision you're describing is essentially a resources system with two categories:

• Internal (chapters, articles): Displayed as formatted content on the site, RAG'd with clean text (sidenotes handled properly)
• External (videos, podcasts, URLs): Displayed as a link + extracted source text, RAG'd from that text

This is a great direction but also a larger project. Let me check what we need for Tuesday vs. what can come later.

Clear. Let me finalize the plan with those decisions reflected.

The plan is ready. Summary:

1. Transcription tool (tools/transcribe/) — yt-dlp + WhisperX pipeline, general-purpose
2. Fix prompt builders — remove hardcoded McLuhan text from preparation-evaluator.ts
3. AlphaGo content — transcript stored as MDX reading (auto-RAG'd), preparation chat that assumes only the film was watched but encourages broader connections

Content is stored in a way that can be reorganized into a resources page later without breaking anything.