Borrowing it
Nothing to install: this file belongs to TheSmokeDev/taskchad-os. 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/TheSmokeDev/taskchad-os/master/.claude/skills/yt-livestream/SKILL.mdgit clone --depth 1 https://github.com/TheSmokeDev/taskchad-osWrote 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/thesmokedev/taskchad-os/yt-livestream)<a href="https://agentmods.dev/skills/thesmokedev/taskchad-os/yt-livestream"><img src="https://agentmods.dev/badge/skills/thesmokedev/taskchad-os/yt-livestream/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/thesmokedev/taskchad-os/yt-livestream"><img src="https://agentmods.dev/badge/skills/thesmokedev/taskchad-os/yt-livestream.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00070 | $0.02101 |
| Opus 5 | $0.00035 | $0.01051 |
| Sonnet 5 | $0.00014 | $0.00420 |
| Haiku 4.5 | $0.00007 | $0.00210 |
Grade A, and why
yt-livestream 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 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.
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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Livestream Content Extractor
Extract repurposable content segments from YouTube livestream transcripts. Identify long-form video opportunities and Shorts moments with precise timestamps.
Philosophy
Livestreams are goldmines of content - hours of authentic teaching, demos, reactions, and insights. The challenge is finding the segments that work as standalone pieces. You're looking for:
Clear boundaries - Where a topic starts and ends naturally Standalone value - Segments that make sense without the surrounding context High-density moments - The exciting, insightful, or visually impressive parts
This isn't about summarizing the stream. It's about identifying extractable segments that could become their own content pieces.
Defaults
| Setting | Default | Notes |
|---|---|---|
| Long-form videos | 3 | Configurable via user request |
| Shorts | 6 | Configurable via user request |
| Output | Content-Ideation/Livestream/YYYY-MM-DD/ |
Single markdown file |
If the user specifies different counts (e.g., "give me 5 video ideas and 10 shorts"), use their numbers.
Workflow
1. Read the Transcript
Read the full transcript file the user provides. Livestreams are typically 2-4 hours long, so the transcript will be substantial.
As you read, mentally note:
- Topic shifts (when the conversation moves to something new)
- Energy peaks (excitement, emphasis, "this is important" moments)
- Demo segments (showing something working, building something)
- Explanatory chunks (clear teaching moments)
- Hot takes or opinions (strong viewpoints that stand alone)
- Results/reveals (impressive outcomes, "and here's what we get")
2. Identify Long-Form Video Segments
Target length: 5-30 minutes of content (typically 10-20 minutes works best)
Look for segments with clear topical boundaries - a natural start and end where the content is self-contained.
What makes a good long-form segment:
| Type | What to Look For |
|---|---|
| Tutorial/Build | Step-by-step walkthrough with setup, implementation, and result |
| Deep Dive | Extended exploration of a single topic, tool, or concept |
| New Tech Discussion | Introduction and breakdown of a new framework, tool, or approach |
| Problem → Solution | Debugging session, architectural decision, or challenge overcome |
| Comparison/Analysis | Evaluating options, trade-offs, or approaches |
| Q&A Segment | Answering viewer questions with substantial depth |
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 · 239 lines · 70 tokens per session scan A d031637e9efa
yt-livestream is a skill published in the GitHub repository TheSmokeDev/taskchad-os (23 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 2,101 once invoked, about $0.0003 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 2026-09-03.
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