Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vre/flow-state --skill fullgit clone --depth 1 https://github.com/vre/flow-stateWrote 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/skills/vre/flow-state/full)<a href="https://agentmods.dev/skills/vre/flow-state/full"><img src="https://agentmods.dev/badge/skills/vre/flow-state/full/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/skills/vre/flow-state/full"><img src="https://agentmods.dev/badge/skills/vre/flow-state/full.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 | $0.00035 | $0.01757 |
| Opus 5.5 | $0.00014 | $0.00703 |
| Sonnet 5.5 | $0.00007 | $0.00351 |
| Haiku 4.5 | $0.00003 | $0.00176 |
Grade A, and why
youtube-to-markdown 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.
This is a copy
100% identical to youtube-to-markdown — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube to Markdown
Multiple videos: Process one video at a time, sequentially. Do not run parallel extractions. Execute all steps sequentially without asking for user approval. Use TodoWrite to track progress.
Step 0: Check extracted before
python3 ./check_existing.py "<YOUTUBE_URL>" "<output_directory>"
Integrity check:
- If
summary_valid: false: Show issues to user, ask "Tiedosto epätäydellinen: [issues]. Prosessoidaanko uudelleen?" If yes, continue to Step 1. - If
transcript_valid: false: Ask user, if yes re-run Steps 2-3, 7-9. - If
comments_valid: false: Ask user, if yes re-run comment analysis.
If returns exists: true AND all valid fields are true: Read and follow UPDATE_MODE.md for update workflow.
Step 1: Extract data (metadata, description, chapters)
python3 extract_data.py "<YOUTUBE_URL>" "<output_directory>"
Creates: youtube_{VIDEO_ID}metadata.md, youtube{VIDEO_ID}description.md, youtube{VIDEO_ID}_chapters.json
IMPORTANT: If you ask which language transcript to extract then do not translate that language to english and require that subagent do not translate either. Only if the user requests another language that the original then translate.
Step 2: Extract transcript
Primary method (if transcript available)
If video language is en, proceed directly. If non-English, ask user which language to download.
python3 extract_transcript.py "<YOUTUBE_URL>" "<output_directory>" "<LANG_CODE>"
Creates: youtube_{VIDEO_ID}_transcript.vtt
IMPORTANT: All file output must be in the same language as discovered in Step 2. If language is not English, explicitly instruct all subagents to preserve the original language.
The download may fail if a video is private, age-restricted, or geo-blocked.
Fallback (only if transcript unavailable)
Ask user: "No transcript available. Proceed with Whisper transcription?
- Mac/Apple Silicon: Uses MLX Whisper if installed (faster, see SETUP_MLX_WHISPER.md)
- All platforms: Falls back to OpenAI Whisper (requires: brew install openai-whisper OR pip3 install openai-whisper)"
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 · 216 lines · 35 tokens per session scan A 22ab5ecd5b77
youtube-to-markdown is a skill published in the GitHub repository vre/flow-state (11 stars, last pushed 25d ago), licensed MIT. It adds 35 tokens to every session and 1,757 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to youtube-to-markdown, differing in 0 lines, and is treated as a copy.
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