YouTube AnyCaption Summarizer

---

name: youtube-anycaption-summarizer

description: "Turn YouTube videos into dependable markdown transcripts and polished summaries — even when caption coverage is messy. This skill works with manual closed captions (CC), auto-generated subtitles, or no usable subtitles at all by using subtitle-first extraction with local Whisper fallback. Supports private/restricted videos via cookies, batch processing, transcript cleanup, language backfill, source-language or user-selected summary language, and end-to-end completion reporting. Ideal for YouTube research, technical walkthroughs, founder content, tutorials, private/internal uploads, and batch video summarization workflows."

metadata: {"openclaw":{"homepage":"https://github.com/arthurli202602-commits/youtube-anycaption-summarizer","requires":{"bins":["yt-dlp","ffmpeg","whisper-cli","python3"]},"install":[{"id":"brew-yt-dlp","kind":"brew","formula":"yt-dlp","bins":["yt-dlp"],"label":"Install yt-dlp (brew)"},{"id":"brew-ffmpeg","kind":"brew","formula":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg (brew)"},{"id":"brew-whisper-cpp","kind":"brew","formula":"whisper-cpp","bins":["whisper-cli"],"label":"Install whisper.cpp CLI (brew)"}]}}

---

# YouTube AnyCaption Summarizer

**The YouTube summarizer that still works when captions are broken, missing, or inconsistent.**

Outputs: raw markdown transcript + polished markdown summary + session-ready result block.

Unlike caption-only tools, this skill still works when subtitles are missing by falling back to local Whisper transcription.

Generate a raw transcript markdown file and a polished summary markdown file from one or more YouTube videos.

This skill is self-contained. It does not require any other YouTube summarizer skill or prior workflow context.

Best for

Why choose this over simpler transcript skills?

Install dependencies

For a fresh macOS setup, new users should be able to copy-paste the following exactly:

brew install yt-dlp ffmpeg whisper-cpp
MODELS_DIR="$HOME/.openclaw/workspace"
MODEL_PATH="$MODELS_DIR/ggml-medium.bin"
mkdir -p "$MODELS_DIR"
if [ ! -f "$MODEL_PATH" ]; then
  curl -L https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-medium.bin \
    -o "$MODEL_PATH.part" && mv "$MODEL_PATH.part" "$MODEL_PATH"
else
  echo "Model already exists at $MODEL_PATH — leaving it unchanged."
fi
command -v python3 yt-dlp ffmpeg whisper-cli
ls -lh "$MODEL_PATH"

What this does:

If you want to store models elsewhere, pass `--models-dir /path/to/models` when running the workflow.

Example requests

Quick start

Single video

python3 scripts/run_youtube_workflow.py "https://www.youtube.com/watch?v=VIDEO_ID"

This creates a dedicated per-video folder, writes the raw transcript markdown, creates the summary placeholder markdown, and prints JSON describing the outputs plus the exact follow-up commands/prompts needed to finish the summary step.

Important: the workflow script alone is not the finished deliverable. The current OpenClaw session must still:

1. infer/backfill the language if the workflow left it as `unknown`

2. overwrite the placeholder `Summary.md` with a real polished summary

3. run `scripts/complete_youtube_summary.py` to validate/finalize the result

Force simplified Chinese summary

python3 scripts/run_youtube_workflow.py "https://www.youtube.com/watch?v=VIDEO_ID" \
  --summary-language zh-CN

Restricted video with cookies

python3 scripts/run_youtube_workflow.py "https://www.youtube.com/watch?v=VIDEO_ID" \
  --cookies /path/to/cookies.txt

or

python3 scripts/run_youtube_workflow.py "https://www.youtube.com/watch?v=VIDEO_ID" \
  --cookies-from-browser chrome

Batch / queue mode

See `references/batch-input-format.md`.

python3 scripts/run_youtube_workflow.py --batch-file ./youtube-urls.txt

Why this skill stands out

This skill is designed to keep working across the messy reality of YouTube:

That makes it materially more reliable than caption-only workflows. It works well for caption-rich videos, caption-poor videos, and private/internal uploads where subtitle coverage is inconsistent.

Core capabilities:

What this skill produces

For each video, create exactly one dedicated output folder containing these final deliverables:

By default, delete only the known intermediate media, subtitle, and WAV files created by the workflow. Do not wipe unrelated files that may already exist in the per-video folder.

Required local tools

Verify these tools exist before running the workflow:

The workflow also requires a supported Whisper ggml model file in the configured models directory.

Bundled scripts

Use these scripts directly:

Useful references:

Defaults

Public workflow overview

At a high level, the skill does this:

1. fetch metadata first and create safe output paths

2. try manual subtitles, then auto-captions, then local Whisper fallback

3. write `SANITIZED_VIDEO_NAME_transcript_raw.md`

4. create `SANITIZED_VIDEO_NAME_Summary.md` as a placeholder

5. have the current OpenClaw session overwrite the placeholder with a real summary

6. run `scripts/complete_youtube_summary.py` to validate completion, backfill language if needed, and emit a session-ready result block

What counts as completion

For a normal end-to-end request, completion means all of the following are true:

1. the workflow script succeeded

2. if language was initially `unknown`, the language was backfilled into both markdown files

3. the placeholder summary file was overwritten with a real summary

4. `scripts/complete_youtube_summary.py` was run successfully

5. the user received the resulting output paths and timing/result status

If the workflow script succeeded but the summary/completion step did not happen yet, describe the state as partial/in-progress rather than complete.

When to read the deeper references

Read these as needed:

Practical public promise

This skill is optimized for dependable end-to-end output, not just quick transcript extraction: