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 RightBlogger/bloggingskills --skill youtube-to-bloggit clone --depth 1 https://github.com/RightBlogger/bloggingskillsWrote 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/rightblogger/bloggingskills/youtube-to-blog)<a href="https://agentmods.dev/skills/rightblogger/bloggingskills/youtube-to-blog"><img src="https://agentmods.dev/badge/skills/rightblogger/bloggingskills/youtube-to-blog/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/rightblogger/bloggingskills/youtube-to-blog"><img src="https://agentmods.dev/badge/skills/rightblogger/bloggingskills/youtube-to-blog.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 | $0.00131 | $0.01150 |
| Opus 5 | $0.00066 | $0.00575 |
| Sonnet 5 | $0.00026 | $0.00230 |
| Haiku 4.5 | $0.00013 | $0.00115 |
Grade A, and why
youtube-to-blog 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube to blog
You turn a video into a blog post that earns its own search traffic. The transcript is raw material, not the draft. You reorganize its ideas around what a reader came to find, add context the camera skipped, and run the same pipeline the article skill uses: outline, section-by-section drafting, then a humanize pass.
Initial assessment
- Read
.agents/blog-context.mdfor niche, audience, pillars, and voice. If it doesn't exist, ask the three essentials inline (audience, goal, voice) or suggest running blog-context first. - Read the house-style rulebook at
../house-style/references/ai-tells.md(it also sits at../article/references/ai-tells.md, since this skill builds on the article pipeline and expects both installed alongside). That file is the full contract; do not write in slop. - Confirm the inputs: the video URL or pasted transcript, the length tier, and whether the user wants the video embedded in the post.
Length tiers (target word counts and heading count)
short ~1,000 (about 10 headings) · medium ~1,500 (about 15) · long ~2,500 (about 25) · very-long ~3,500 (about 40). Hold the final draft within roughly 90 to 105% of target and trim before returning.
Method
1. Get the transcript
If the user gave a URL and a YouTube tool or MCP is available, pull the transcript that way. Otherwise ask the user to paste it. Either way, capture the video title and channel name so you can attribute the source later.
2. Build an outline from the transcript's ideas
Mine the transcript for its substance, then reorganize it around reader search intent rather than the order the speaker happened to cover things. A video meanders; a post answers the question the reader typed. Reuse the article skill's outline approach in ../article/references/outline.md: intent-driven headings that describe what the reader learns, the primary keyword or a variant in at least one heading, related terms spread across the rest. Scale the heading count to the length tier above. Offer the user a checkpoint here if they want one.
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 · 95 lines · 131 tokens per session scan A 945f159ff372
youtube-to-blog is a skill published in the GitHub repository RightBlogger/bloggingskills (2 stars, last pushed 2mo ago), licensed MIT. It adds 131 tokens to every session and 1,150 once invoked, about $0.0007 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 2026-08-31.
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