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 agentmods add skills/vre/flow-state/modulesnpx skills add vre/flow-state --skill modulesgit clone --depth 1 https://github.com/vre/flow-stateWhat 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 | $0.00035 | $0.00481 |
| Opus 5 | $0.00017 | $0.00241 |
| Sonnet 5 | $0.00007 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
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 yesterday.
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.
What it actually says
YouTube to Markdown
Multiple videos: Process one video at a time, sequentially. Do not run parallel extractions. Do not create your own scripts.
Step 0: Check if extracted before
python3 ./check_existing.py "<YOUTUBE_URL>" "<output_directory>"
If any *_valid: false: Show the issues to user and proceed.
Output JSON contains video_id. Set BASE_NAME = youtube_{video_id} for all subsequent steps.
Step 1: Choose output
AskUserQuestion:
- question: "What do you want to extract from the video?"
- header: "Output"
- multiSelect: false
- options: A. "Summary only" - Tight summary of video content B. "Transcript only" - Cleaned, formatted full transcript C. "Comments only" - Curated comments D. "Summary + Comments" - Summary with cross-analyzed comment insights E. "Full (Recommended)" - All: summary, transcript, comments
Step 2: Execute modules
Based on user's choice, read and follow each module instruction in ./modules/{file}. "|" marks possibility to run concurrently.
- A: transcript_extract.md → transcript_summarize.md
- B: transcript_extract.md → transcript_polish.md
- C: comment_extract.md
- D: transcript_extract.md → (transcript_summarize.md | comment_extract.md) → comment_summarize.md
- E: transcript_extract.md → (transcript_summarize.md | transcript_polish.md | comment_extract.md) → comment_summarize.md
Step 3: Finalize
python3 finalize.py [flag] "${BASE_NAME}" "<output_directory>"
Flags: A=--summary-only, B=--transcript-only, C=--comments-only, D=--summary-comments, E=(none)
Use --debug to keep intermediate files.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 59 lines · 35 tokens per session scan A b01c9c4fc4ee
youtube-to-markdown is a skill published in the GitHub repository vre/flow-state (12 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 481 once invoked, about $0.0002 per session on Opus 5. 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.
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