Borrowing it
Nothing to install: this file belongs to velmighty/youtube-to-knowledge. 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/velmighty/youtube-to-knowledge/main/.claude/commands/process.mdgit clone --depth 1 https://github.com/velmighty/youtube-to-knowledgeWrote 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/commands/velmighty/youtube-to-knowledge/process)<a href="https://agentmods.dev/commands/velmighty/youtube-to-knowledge/process"><img src="https://agentmods.dev/badge/commands/velmighty/youtube-to-knowledge/process/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/commands/velmighty/youtube-to-knowledge/process"><img src="https://agentmods.dev/badge/commands/velmighty/youtube-to-knowledge/process.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 | $0.00000 | $0.01303 |
| Opus 5.5 | $0.00000 | $0.00521 |
| Sonnet 5.5 | $0.00000 | $0.00261 |
| Haiku 4.5 | $0.00000 | $0.00130 |
Grade A, and why
process 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 1mo 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process one or more YouTube videos (or a playlist) into structured knowledge.
Arguments: $ARGUMENTS
Parse $ARGUMENTS to extract:
- URLs: All arguments that start with
httpor look like YouTube video IDs. There may be one or many. - Playlist: If a URL contains
playlist?list=, it is a playlist — expand it first (see Step 0). - --depth:
light|standard|deep(default:standard). - --engine:
whisperx|whisper(default:whisper). - --obsidian: flag (no value). If present, export entities as Obsidian markdown notes.
Examples:
/process https://youtube.com/watch?v=xyz/process https://youtube.com/watch?v=abc https://youtube.com/watch?v=def/process https://www.youtube.com/playlist?list=PLxxx/process --depth deep https://youtube.com/watch?v=xyz/process --obsidian https://youtube.com/watch?v=xyz
Follow these steps exactly:
Step 0 — Expand playlist (only if a playlist URL was given)
Run: python src/playlist_extractor.py <playlist_url>
Each line of stdout is a video URL. Replace the playlist URL in your working list with these individual URLs. Proceed to Step 1 for each video sequentially.
Step 1 — Duplicate check
If processing multiple videos: announce "Processing X videos" before starting.
Read vault/processed_videos.md. Extract the video ID from the URL. If the ID appears in the file, skip this video (print "SKIPPED: already processed") and move to the next one.
Step 2 — Transcription
Run: python src/transcribe.py <URL>
Parse stdout for:
CHANNEL_DIR:<path>RAW_FILE:<path>SOURCE_LANG:<lang>TITLE:<title>ENRICHED_FILE:<path>(optional, only from WhisperX)
If the script exits with error code 1 (no transcript found):
- If engine is
whisper(default): runpython src/transcribe_whisper.py <URL> - If engine is
whisperx: runpython src/transcribe_whisperx.py <URL> - If the chosen engine also fails, try the other as a last resort.
Parse the same output variables from whichever script succeeds.
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.
- 1mo ago First seen · 132 lines · 0 tokens per session scan A b695785c5697
process is a command published in the GitHub repository velmighty/youtube-to-knowledge (66 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,303 tokens. 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 2026-08-30.
Other commands, from other repositories
maestro-learn
User-invoked learning toolkit — guided reading, investigation, pattern extraction, or second opinions. Manual /maestro-learn only; NEVER auto-invoke for code exploration or analysis — route those intents to the analyze step via /maestro-next.
lesson
Set up, see what you know, or get the lesson that's waiting.
voxpip
Watch any video and get a transcript — even without an API key. Extends /watch with a local speech-to-text fallback.
start-0-8
A Japanese-language command that displays the current project setup progress.
prep-role
Deep prep for a specific role's interview process. The user wants to know what to expect, how to win, and what kills candidates at their level.
start-1
A guided beginner course for product managers learning Claude Code, a coding assistant that works from the command line.