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

• - founder videos, operator walkthroughs, and technical explainers
• long tutorial videos that need transcript + implementation summary
• private/internal YouTube uploads that may require cookies
• mixed-caption environments where some videos have CC, some only have auto-captions, and some have no usable subtitles
• batch research workflows where many YouTube links need standardized markdown outputs
• users who want reliable markdown artifacts, not just a one-off chat summary

Why choose this over simpler transcript skills?

• - manual CC first, auto-captions second, local Whisper fallback last
• keeps working when subtitle coverage is weak or missing
• supports private/restricted YouTube videos via cookies
• returns durable markdown artifacts, not just chat text
• supports batch processing and session-ready completion reporting

Install dependencies

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

CODEBLOCK0

What this does:

• - installs yt-dlp, ffmpeg, and INLINECODE2
• creates the default models directory used by this skill if it does not already exist: INLINECODE3
• downloads the default Whisper model file only if it is missing
• avoids touching ~/.openclaw/openclaw.json or any other OpenClaw config file
• does not delete, replace, or overwrite other files in your existing workspace folder
• verifies that the required binaries and model file are present

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

Example requests

• - “Summarize this YouTube video into markdown.”
• “Generate a transcript and polished summary for this YouTube link.”
• “Process this private YouTube video with my browser cookies.”
• “Batch summarize these YouTube links and give me transcript + summary files.”
• “Use subtitles when available, otherwise transcribe locally.”
• “Create a Chinese summary from this English YouTube video.”

Quick start

Single video

CODEBLOCK1

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. 1. infer/backfill the language if the workflow left it as INLINECODE6
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

CODEBLOCK2

Restricted video with cookies

CODEBLOCK3

or

CODEBLOCK4

Batch / queue mode

See references/batch-input-format.md.

CODEBLOCK5

Why this skill stands out

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

• - if a video has manual closed captions (CC), use them first
• if it only has auto-generated subtitles, use those next
• if it has no usable subtitles at all, fall back to local Whisper transcription

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:

• - fetch YouTube metadata first and derive safe output paths
• support single-video mode and batch / queue mode
• handle manual CC, auto-generated subtitles, or no subtitles via subtitle-first extraction with local Whisper fallback
• support restricted/private videos via cookies or browser-cookie extraction
• normalize noisy transcript text before summarization
• create a placeholder summary file, overwrite it with the final summary, and finalize end-to-end timing
• clean up only known intermediates created by the workflow unless explicitly told otherwise

What this skill produces

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

• - INLINECODE10
• INLINECODE11

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:

• - INLINECODE12
• INLINECODE13
• INLINECODE14
• INLINECODE15

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

Bundled scripts

Use these scripts directly:

• - scripts/run_youtube_workflow.py — main deterministic workflow for metadata, download/subtitles, transcription, placeholder summary creation, cleanup, and workflow metadata emission
• INLINECODE17 — update transcript_raw.md, Summary.md, and workflow metadata after the current session LLM decides the major transcript language
• INLINECODE20 — validate that Summary.md is no longer a placeholder, optionally backfill language, compute the final end-to-end timing report for one item, and emit a session-ready result block
• INLINECODE22 — convert raw timestamped transcript text into cleaner summary input without modifying the raw transcript file
• INLINECODE23 — lower-level timing helper used by the completion flow
• INLINECODE24 — derive sanitized folder and output file paths from a title and video ID

Useful references:

• - references/detailed-workflow.md — full operational workflow, completion rules, batch guidance, naming rules, and practical notes
• INLINECODE26 — required structure and writing rules for the final INLINECODE27
• INLINECODE28 — required user-facing output format to return to the current OpenClaw session after completion
• INLINECODE29 — input format for queue / batch processing

Defaults

• - Default parent output folder: INLINECODE30
• Default whisper model: INLINECODE31
• Supported whisper models: ggml-base, ggml-small, INLINECODE34
• Default media mode: audio-only
• Default transcript language: auto-detect if transcription is needed
• Default summary language: INLINECODE35
• Raw transcript keeps timestamps

Public workflow overview

At a high level, the skill does this:

1. 1. fetch metadata first and create safe output paths
2. try manual subtitles, then auto-captions, then local Whisper fallback
3. write INLINECODE36
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. 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. INLINECODE40 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:

• - references/detailed-workflow.md when you need the full implementation contract, batch guidance, naming rules, cleanup rules, timing flow, or debugging details
• INLINECODE42 before writing the final polished INLINECODE43
• INLINECODE44 before returning the final user-facing per-video result block
• INLINECODE45 when handling INLINECODE46

Practical public promise

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

• - raw transcript markdown
• polished summary markdown
• session-ready completion report