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 aragaobruno/toolbelt --skill schema-markup-buildergit clone --depth 1 https://github.com/aragaobruno/toolbeltWrote 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/aragaobruno/toolbelt/schema-markup-builder)<a href="https://agentmods.dev/skills/aragaobruno/toolbelt/schema-markup-builder"><img src="https://agentmods.dev/badge/skills/aragaobruno/toolbelt/schema-markup-builder/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/aragaobruno/toolbelt/schema-markup-builder"><img src="https://agentmods.dev/badge/skills/aragaobruno/toolbelt/schema-markup-builder.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 | $0.00000 | $0.00499 |
| Opus 5 | $0.00000 | $0.00249 |
| Sonnet 5 | $0.00000 | $0.00100 |
| Haiku 4.5 | $0.00000 | $0.00050 |
Grade A, and why
schema-markup-builder 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 8d 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.
What it actually says
🌐 Skill: schema-markup-builder
An automated utility skill to build valid, highly optimized schema.org JSON-LD structured data for technical SEO. Excellent for optimizing Local Business visibility, organizational SEO, and product schema.
🛠️ Triggers
This skill is triggered when an agent or developer needs to generate structured JSON-LD schemas, build SEO markups, or prepare rich snippet scripts for websites.
- "generate seo schema"
- "build json-ld schema"
- "run schema-markup-builder"
- "create localbusiness schema"
🚀 Usage Guide
Requirements
- Python (3.10+)
google-genaipackage installed (viauv)- A valid
GEMINI_API_KEYdefined in the environment or in the global.hermes/.envfile.
Command Execution
Run the script using uv run from the repository root:
# Basic LocalBusiness schema
uv run --with google-genai skills/integration/schema-markup-builder/scripts/builder.py \
--business "Bruno's Coffee Shop" \
--url "https://brunoscoffeeshop.com"
# Custom Schema Type with extra parameters
uv run --with google-genai skills/integration/schema-markup-builder/scripts/builder.py \
--business "Bruno's Coffee Shop" \
--url "https://brunoscoffeeshop.com" \
--type "Cafe" \
--params '{"telephone": "+1 212-555-0199", "address": {"streetAddress": "123 Main Street", "addressLocality": "New York", "addressRegion": "NY", "postalCode": "10001", "addressCountry": "US"}}' \
--output "docs/cafe_schema.json"
Options
-b,--business: Name of the business (required).-u,--url: Main website URL (required).-t,--type: Schema.org type, e.g.,LocalBusiness,HairSalon,Organization,Product(default:LocalBusiness).-p,--params: Path to a JSON file or a raw JSON string containing extra parameters.-o,--output: Target path to save the generated JSON file (default:schema.json).
What ships with it
1 file 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.
- 8d ago First seen · 48 lines · 0 tokens per session scan A 560b993173ff
schema-markup-builder is a skill published in the GitHub repository aragaobruno/toolbelt (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 499 tokens. 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.
Other skills, from other repositories
ai-project-starter
Use when the user wants to prepare project-specific context engineering documents, AI coding agent instruction files, project starter Markdown files, vibe coding guardrails, PRD/TDD/architecture/security/testing docs, AGENTS.md/CLAUDE.md/Cursor/Windsurf/Continue/Copilot context files, or a complete AI-coding-ready…
repo-harness
Route explicit repo-harness setup, planning, execution, verification, and handoff actions through deterministic repository state.
review
Validate plans, execution, or PRs against wish criteria — returns SHIP / FIX-FIRST / BLOCKED with severity-tagged gaps.
work
Execute an approved wish plan — orchestrate subagents per task group with fix loops, validation, and review handoff.
brainstorm
Explore ambiguous or early-stage ideas interactively — tracks wish-readiness and crystallizes into a design for wish.
report
Investigate bugs comprehensively — cascade through trace, capture browser evidence, extract observability data, and prepare or explicitly create a GitHub issue with grounded findings.