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 T4wroot/agentic-seo --skill content-optimizationgit clone --depth 1 https://github.com/T4wroot/agentic-seoWrote 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/t4wroot/agentic-seo/content-optimization)<a href="https://agentmods.dev/skills/t4wroot/agentic-seo/content-optimization"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/content-optimization/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/t4wroot/agentic-seo/content-optimization"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/content-optimization.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 | $0.00077 | $0.02005 |
| Opus 5 | $0.00039 | $0.01002 |
| Sonnet 5 | $0.00015 | $0.00401 |
| Haiku 4.5 | $0.00008 | $0.00200 |
Grade A, and why
content-optimization 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.
This is a copy
100% identical to content-optimization — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Content: Content Optimization
Guides on-page content optimization: word count, heading keywords, keyword density vs stuffing, multimedia, tables, and lists. Complements heading-structure (structure) and content-strategy (planning).
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Scope
- Word count: For articles, see article-content (word count by type). This skill covers generic content length strategy.
- H2 keywords: Placement, quantity, variation
- Keyword density vs stuffing: Natural use; avoid manipulation
- Multimedia: Images, tables, lists, video for structure and Featured Snippets. See featured-snippet for snippet-specific optimization; video-optimization for video SEO.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for target keywords and content type.
Identify:
- Content type: Article, guide, listicle, pillar, news
- Target keyword: Primary and secondary
- Competitors: Top 10 average length and structure — see competitor-research
Word Count
Google does not rank by word count. Length should match search intent and topic depth. A 1,000-word post that satisfies intent can outrank a 3,000-word thin piece.
Reference Ranges by Content Type
For article word count by type (news, how-to, listicle, pillar, etc.), see article-content. Generic ranges:
| Content type | Word count | Notes |
|---|---|---|
| News / announcements | 300–600 | Time-sensitive; concise |
| Standard articles / how-tos | 1,000–1,500 | Single topic; actionable |
| Listicles / guides | 1,200–2,000 | "Top 10," "Best X" |
| Pillar / cornerstone | 2,000–3,500+ | Comprehensive; cluster hub |
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 · 178 lines · 77 tokens per session scan A f7abbf0cfe40
content-optimization is a skill published in the GitHub repository T4wroot/agentic-seo (15 stars, last pushed 8d ago), licensed MIT. It adds 77 tokens to every session and 2,005 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to content-optimization, differing in 0 lines, and is treated as a copy.
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