YouTube Video to Blog Post: My 3-Step Repurposing Pipeline

Turning a YouTube video to blog post form takes me about 25 minutes now, down from the three hours it took back when I typed notes against the playback. The pipeline is three steps: pull the transcript through an API, run one structured prompt, then do a human edit pass that is not optional. Here's each step, including the exact prompt template I use.

Step 1: Pull the transcript (30 seconds)

One GET request against youtube2text.org does it. Grab a demo key first — no signup:

curl -s "https://youtube2text.org/api/demo-key"
# {"success":true,"apiKey":"yt_..."}

curl -s "https://youtube2text.org/api/transcribe?url=https://www.youtube.com/watch?v=VIDEO_ID" \
  -H "x-api-key: yt_your_key"

You get JSON back with result.content (the transcript) plus title, pubDate, and contentSize. Useful sizing math: a 15-minute tutorial runs around 18k characters, a 40-minute talk lands at 45-60k. Both fit in a single Claude or GPT context under the default maxChars of 150000, so there's no chunking step to build. If the video has no captions at all you get a TRANSCRIPT_UNAVAILABLE 404, which is rarer than you'd expect — auto-captions cover most public uploads. More request variations are in the curl walkthrough.

Step 2: One structured prompt beats five vague ones

My early mistake was prompting "turn this into a blog post" and getting back a book report. What works is pinning down the reader, the keyword, and the rules:

You are converting a video transcript into a blog post.

Video title: {TITLE}
Target reader: {WHO THIS POST IS FOR}
Primary keyword: {KEYWORD}
Length: 900-1200 words, markdown, H2 sections

Rules:
- Keep the speaker's examples and numbers exactly. Invent nothing.
- Rewrite spoken filler into prose. No "in this video" phrasing.
- Preserve 2-3 direct quotes worth keeping, as blockquotes.
- End with a three-bullet summary and one concrete next step.
- Mark anything you are unsure about — names, product terms,
  figures that look mis-transcribed — with [CHECK].

Transcript:
{PASTE result.content HERE}

The [CHECK] rule is the workhorse. It turns the model from a confident fabricator into a collaborator that flags its own doubts, and it gives your edit pass a search target.

Step 3: The edit pass nobody gets to skip

Now the honest part. Transcripts come from YouTube's captions, and auto-generated captions arrive without reliable punctuation and with a real talent for mishearing proper nouns. I once had "Kubernetes" transcribed as "communities" four times in one video, and the LLM cheerfully wrote a paragraph about deployment communities. Budget 10-15 minutes to search for every [CHECK], fix names and product terms against the actual video, and cut the one paragraph where the model gets philosophical. Videos with manually uploaded captions need less of this, but review before publishing either way.

Scaling from one video to twenty

The same three steps handle a backlog — the only thing that changes is quota. The shared demo key caps at 5 videos per month per IP, which covers a trial run and not much else. If you're mining a competitor's whole catalog, I wrote up that workflow in YouTube content research; if your source material is interview-style, the prompt needs the quote-handling tweaks from podcast show notes.

For anything beyond the trial, get your own key with a Google sign-in at youtube2text.org/app/keys — the free tier gives you 5 videos a month, $5.99 gets 50, and there's no card or phone number involved. That's one blog post per week from video you already made, for roughly the price of the coffee you'll drink editing it.