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 charlieviettq/awesome-agent-skill --skill algo-seo-schemagit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/algo-seo-schema)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-seo-schema"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-seo-schema/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/charlieviettq/awesome-agent-skill/algo-seo-schema"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-seo-schema.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 | $0.00069 | $0.00860 |
| Opus 5 | $0.00034 | $0.00430 |
| Sonnet 5 | $0.00014 | $0.00172 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
"algo-seo-schema" 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 9d 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
89% identical to algo-seo-schema — 8 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schema.org Structured Data
Overview
Schema.org structured data provides machine-readable page context to search engines via JSON-LD. Enables rich results (stars, FAQs, breadcrumbs, product cards) in SERPs. Implementation is O(1) per page — it's a markup task, not computational.
When to Use
Trigger conditions:
- Adding rich snippet eligibility to web pages
- Implementing product, article, FAQ, HowTo, or event markup
- Debugging Google Search Console structured data errors
When NOT to use:
- When optimizing page content or keywords (use content SEO)
- When improving page speed (use Core Web Vitals optimization)
Algorithm
IRON LAW: Schema Markup Must MATCH Visible Content
Marking up content that users can't see violates Google guidelines
and risks manual penalties. Every structured data field must
correspond to content visible on the page.
Phase 1: Input Validation
Identify page type (Article, Product, FAQ, HowTo, Event, etc.). Map visible content to required and recommended schema properties. Gate: Page type identified, all required properties have visible content.
Phase 2: Core Algorithm
- Select the correct Schema.org type from the vocabulary
- Map page content to schema properties (name, description, image, etc.)
- Build JSON-LD object with @context and @type
- Handle nested types (e.g., Product contains Offer contains Price)
- Place JSON-LD in
<script type="application/ld+json">in<head>
Phase 3: Verification
Validate with Google Rich Results Test. Check: no errors, all required fields present, no mismatch with visible content. Gate: Passes Google Rich Results Test with zero errors.
Phase 4: Output
Return complete JSON-LD markup ready for insertion.
Output Format
{
"schema": {"@context": "https://schema.org", "@type": "Product", "name": "...", "offers": {"@type": "Offer", "price": "29.99", "priceCurrency": "TWD"}},
"validation": {"errors": 0, "warnings": 1, "eligible_rich_results": ["Product snippet"]}
}
What ships with it
3 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.
- 9d ago First seen · 85 lines · 69 tokens per session scan A 900df77db96d
"algo-seo-schema" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 860 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to algo-seo-schema, differing in 8 lines, and is treated as a copy.
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