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 davidtoby/agent-skills --skill consulting-pdf-from-youtubegit clone --depth 1 https://github.com/davidtoby/agent-skillsWrote 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/davidtoby/agent-skills/consulting-pdf-from-youtube)<a href="https://agentmods.dev/skills/davidtoby/agent-skills/consulting-pdf-from-youtube"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/consulting-pdf-from-youtube/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/davidtoby/agent-skills/consulting-pdf-from-youtube"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/consulting-pdf-from-youtube.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 | $0.00076 | $0.12367 |
| Opus 5 | $0.00038 | $0.06184 |
| Sonnet 5 | $0.00015 | $0.02473 |
| Haiku 4.5 | $0.00008 | $0.01237 |
Grade A, and why
consulting-pdf-from-youtube scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sL --max-time 10 "https://inv.nadeko.net/api/v1/videos/<video-id>" How it starts
The opening of the file, as written. The whole thing — 872 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Consulting PDF from YouTube
Core principle: Subtitle-first, Whisper-last — with quality gates at every step.
YouTube auto-generated subtitles (via yt-dlp --write-auto-subs) are available for the vast majority of videos. They download in seconds and avoid the 30–90+ minute Whisper transcription pipeline with its systematic proper-noun errors.
Transcription priority chain:
- 🥇 YouTube auto-subs — download with
yt-dlp --write-auto-subs --sub-langs(seconds) - 🥈 Whisper fallback — if auto-subs fail the quality gate (too sparse, garbled, or absent): download audio → transcribe with faster-whisper → verify quality → verify proper nouns (minutes to hours)
- 🥉 Flag to user — if Whisper output also fails quality check, inform the user and ask whether to proceed with lower-quality output or try alternatives (OpenAI Whisper API, different model size)
Only fall back to Whisper/faster-whisper when:
- The video has zero auto-subs in any language
- The auto-subs fail the quality gate (Step 2b: file size <1KB/min, entries <2/min, or garbled content)
- The user explicitly wants higher transcription accuracy than auto-subs can provide
Use this skill when:
- The user shares a YouTube link and wants a consulting-style PDF report
- The user provides a local video file path (MP4, MOV, etc.) and wants a Chinese consulting report
- The user shares a social-media video URL (X/Twitter, Bilibili, etc.) and wants a consulting report
The deliverable is always:
- consulting-style PDF report (Chinese or English source → Chinese output)
- transcript-based analysis
- key takeaways / insights
- professional visual styling
Also use this skill for social-video consulting reports beyond YouTube (especially X/Twitter videos) when the requested deliverable is still a polished Chinese consulting-style PDF with transcript-based analysis, critical thinking, and/or fact verification. For X/Twitter sources, combine this workflow with x-video-download-normalization: download and verify the MP4 first, then extract audio/keyframes and continue with the report pipeline below.
Also use it for multi-platform public-video evidence reports: when the user asks to research people/topics across YouTube, Bilibili, Douyin/TikTok, Reddit, X, etc.; extract influential quotes/highlights; preserve timestamps/source links; analyze influence/controversy/bans; and deliver a Chinese PDF. In this class of task, treat each quoted item as evidence with an explicit status: 字幕直引 / 章节标题 / 标题或描述定位 / 二级解读 / 未验证，不作证据. Do not invent quotes to fill platform coverage gaps.
Video download is optional for YouTube because metadata + subtitles may be enough. For Bilibili/search-result-only sources, browser DOM extraction of titles, URLs, view counts, durations, and publish dates may be sufficient for a source table, but avoid claiming exact quotes unless subtitles/video content were directly inspected. For Douyin/Reddit or other blocked platforms, record the access limitation and exclude unverifiable claims from the evidence table. For X/Twitter and other social videos without caption APIs, full video download is usually required so you can extract audio and frames.
What ships with it
2 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.
- 7d ago First seen · 872 lines · 76 tokens per session scan A 41775de78de7
consulting-pdf-from-youtube is a skill published in the GitHub repository davidtoby/agent-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 76 tokens to every session and 12,367 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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