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/combinednpx skills add vre/flow-state --skill combinedgit 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 | $0.01238 |
| Opus 5 | $0.00017 | $0.00619 |
| Sonnet 5 | $0.00007 | $0.00248 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
youtube-to-markdown scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube to Markdown (Combined Variant)
Test variant: Keeps all polish steps (4, 7, 8). Combines summary (5+6) and comments (10a+10b) into single subagents.
Execute all steps sequentially without asking for user approval.
Step 0: Check extracted before
python3 ./check_existing.py "<YOUTUBE_URL>" "<output_directory>"
If returns exists: true: Skip to Step 10 (comments).
Step 1: Extract data
python3 extract_data.py "<YOUTUBE_URL>" "<output_directory>"
Step 2: Extract transcript
If video language is en, proceed directly. If non-English, ask user which language.
python3 extract_transcript.py "<YOUTUBE_URL>" "<output_directory>" "<LANG_CODE>"
Fallback (only if transcript unavailable)
python3 extract_transcript_whisper.py "<YOUTUBE_URL>" "<output_directory>"
Step 3: Deduplicate transcript
python3 ./deduplicate_vtt.py "<output_directory>/${BASE_NAME}_transcript.vtt" "<output_directory>/${BASE_NAME}_transcript_dedup.md" "<output_directory>/${BASE_NAME}_transcript_no_timestamps.txt"
Step 4: Add natural paragraph breaks
task_tool:
- subagent_type: "general-purpose"
- model: "sonnet"
- prompt:
INPUT: <output_directory>/${BASE_NAME}_transcript_no_timestamps.txt
CHAPTERS: <output_directory>/${BASE_NAME}_chapters.json
OUTPUT: <output_directory>/${BASE_NAME}_transcript_paragraphs.txt
Analyze INPUT and identify natural paragraph break line numbers.
Read CHAPTERS. If contains chapters, use as primary break points.
Target ~500 chars per paragraph.
Write to OUTPUT: 15,42,78,103,...
python3 ./apply_paragraph_breaks.py "<output_directory>/${BASE_NAME}_transcript_dedup.md" "<output_directory>/${BASE_NAME}_transcript_paragraphs.txt" "<output_directory>/${BASE_NAME}_transcript_paragraphs.md"
Step 5: Summarize transcript (combined summarize + tighten)
task_tool:
- subagent_type: "general-purpose"
- model: "sonnet"
- prompt:
INPUT: <output_directory>/${BASE_NAME}_transcript_no_timestamps.txt
OUTPUT: <output_directory>/${BASE_NAME}_summary_tight.md
FORMATS: ./summary_formats.md
1. Classify content type:
- TIPS: gear reviews, rankings, practical advice
- INTERVIEW: podcasts, conversations, Q&A
- EDUCATIONAL: concept explanations, analysis
- TUTORIAL: step-by-step instructions
2. Read FORMATS and create summary using format for detected type.
3. Self-review: Cut fluff, enforce <10% of transcript bytes, prefer lists over prose.
4. Save final tightened summary to OUTPUT.
Rules:
- Skip ads, sponsors, self-promotion
- Preserve original language
ACTION REQUIRED: Use Write tool NOW to save to OUTPUT file.
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.
- 2d ago First seen · 184 lines · 35 tokens per session scan A 57185d84aaa9
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 1,238 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.
Other skills, from other repositories
ask-zilliz
Zilliz Cloud onboarding and usage assistant. Helps users understand Zilliz Cloud, choose the right plan, estimate costs, write code, debug issues, and adopt new features like Functions, Volumes, and Global Clusters. Use this skill whenever the user asks about Zilliz Cloud — including plan selection, pricing, cost…
diagnose
Use when the user reports that a Zilliz Cloud cluster or Milvus collection is unhealthy, slow, stuck, returning errors, hitting quotas, or otherwise misbehaving — or when they ask "what's wrong with...", "why is ... slow", "diagnose ...", "troubleshoot ...".
collection
Use when the user wants to create, list, describe, drop, rename, load, release, or manage collections and collection aliases in Milvus.
cluster
Use when the user wants to create, list, describe, delete, suspend, resume, or modify Zilliz Cloud clusters.
external-collection
Use when the user wants to trigger, describe, or list refresh jobs for an external collection (a collection backed by an external data source such as Vector Lake). Note this is the data-plane refresh workflow, not collection CRUD -- for create/drop/load see the collection skill.
backup
Use when the user wants to create, list, describe, delete, export, or restore backups, or manage backup policies on Zilliz Cloud.