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 moonlight-lupin/agent-skills --skill youtube-topic-researchgit clone --depth 1 https://github.com/moonlight-lupin/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/moonlight-lupin/agent-skills/youtube-topic-research)<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/youtube-topic-research"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/youtube-topic-research/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/moonlight-lupin/agent-skills/youtube-topic-research"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/youtube-topic-research.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 | $0.00053 | $0.02711 |
| Opus 5 | $0.00026 | $0.01355 |
| Sonnet 5 | $0.00011 | $0.00542 |
| Haiku 4.5 | $0.00005 | $0.00271 |
Grade A, and why
youtube-topic-research 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 12d 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 — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Topic Research Skill
When to Use
Use when the user wants to find and summarize YouTube videos on a specific topic — not when they already have a URL (use a transcript extraction skill for that). This skill searches, filters, fetches transcripts, and returns the top relevant videos with summaries.
Examples:
- "Find YouTube videos on Python async programming"
- "Show me recent videos about LLM fine-tuning"
- "What are the best tutorials for React Server Components?"
- "Research this topic through YouTube, then build a vault" → feeder mode
Two Modes
Standalone mode (default)
Search YouTube, fetch transcripts, rank videos, and return the top recommendations with summaries, freshness indicators, and watch/skip guidance.
NotebookLM feeder mode
After the user approves videos, save each selected video as a source file
compatible with notebooklm-mode, including metadata, URL, transcript
extracts, visual/demo notes, summary, and freshness status.
Trigger phrases for feeder mode:
- "add these to notebooklm"
- "make a source vault from these videos"
- "research this through YouTube first, then build a vault"
- "use videos as sources"
# Feeder mode — export selected videos as notebooklm-mode source files
python scripts/search_and_summarize.py "docker networking" --export-vault /path/to/vault
This generates source files in sources/ inside the vault, formatted for
notebooklm-mode ingestion. The agent can then run notebooklm-mode for
grounded Q&A, notes, reports, or slides built on the video sources.
Architecture
youtube-topic-research
│
├── standalone recommendation output (default)
│
└── --export-vault: selected videos as source files
│
▼
notebooklm-mode vault
│
▼
grounded Q&A / notes / reports / slides
Relationship to notebooklm-mode
This skill can be used standalone or as a feeder into notebooklm-mode:
What ships with it
10 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.
- references/debugging-patterns.md 4.6 KB
- references/fast_moving_domains.yaml 3.2 KB
- references/ip-blocking-workaround.md 4.2 KB
- references/qualify_prompt.md 2.7 KB
- references/review_prompt.md 3.0 KB
- requirements.txt 306 B
- scripts/search_and_summarize.py 41 KB runs code
- templates/output.md.j2 1.7 KB
- tests/__init__.py 0 B runs code
- tests/test_search_and_summarize.py 35 KB runs code
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.
- 12d ago First seen · 322 lines · 53 tokens per session scan A ee8e79f87c6d
youtube-topic-research is a skill published in the GitHub repository moonlight-lupin/agent-skills (62 stars, last pushed 4d ago), licensed MIT. It adds 53 tokens to every session and 2,711 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 2026-08-30.
Other skills, from other repositories
SoloFlow
A meta-skill that silently watches your workflows and automatically generates reusable Hermes skills from them.
hermes-tweet
Use Xquik in Hermes Agent for public X research, monitoring, thread summaries, creator discovery & approved actions. Not affiliated with X Corp. Use when the user requests X data or a named action. Trigger with "search X", "monitor X", "post tweet", or "X trends".
image
Create or optimize marketing images, social graphics, product mockups, banners, cover art, listing visuals, brand assets, image prompts, WebP files, and OG images.
video
Plan and produce video with available AI tools or programmatic frameworks. Use for video prompts, avatars, explainers, demos, templates, pipelines, and generation.
content-research-writer
Research, outline, draft, cite, and revise articles, reports, tutorials, newsletters, and case studies with traceable sources and preserved voice.
content-analysis
Analyze articles, documents, videos, podcasts, and transcripts for arguments, evidence, themes, assumptions, contradictions, insights, and unanswered questions.