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 murphytrueman/design-system-ops --skill metadata-schema-generatorgit clone --depth 1 https://github.com/murphytrueman/design-system-opsWrote 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/murphytrueman/design-system-ops/metadata-schema-generator)<a href="https://agentmods.dev/skills/murphytrueman/design-system-ops/metadata-schema-generator"><img src="https://agentmods.dev/badge/skills/murphytrueman/design-system-ops/metadata-schema-generator.svg" alt="Measured on agentmods" 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 | $0.00139 | $0.03322 |
| Opus 5 | $0.00069 | $0.01661 |
| Sonnet 5 | $0.00028 | $0.00664 |
| Haiku 4.5 | $0.00014 | $0.00332 |
Grade A, and why
metadata-schema-generator 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.
How it starts
The opening of the file, as written. The whole thing — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metadata schema generator
A skill for generating structured JSON metadata schemas for design system components. These schemas encode everything an AI agent, MCP server, code generator, or testing framework needs to work with a component programmatically — props, behavioural rules, composition constraints, accessibility contracts, and business context — in a format that does not require natural language parsing.
Context
Component documentation serves humans. Component metadata serves machines. The distinction matters because the information needs are different, the format requirements are different, and the failure modes are different.
A human reading a component's documentation can infer that "this button triggers the primary action" means it should be visually prominent and placed in the expected location. A code generation agent reading that same string cannot infer any of that — it needs explicit data: { "role": "primary_action", "visual_weight": "high", "placement": ["form_footer", "dialog_footer", "page_header"] }.
Most design systems have one layer of machine-readable data: TypeScript interfaces or PropTypes declarations that define prop names and types. This is necessary but insufficient. A TypeScript interface tells a tool that a Button accepts a variant prop of type "primary" | "secondary" | "ghost" — but it does not tell the tool when to use primary vs secondary, which combinations of props are prohibited, where the component can be placed in a layout, or what accessibility contract it must honour.
Structured metadata fills this gap. It encodes the knowledge layer between "what the component accepts" (TypeScript) and "how the component should be used" (documentation) as machine-readable data that tooling consumes directly.
The ai-component-description skill produces text descriptions optimised for LLM consumption. This skill produces structured data optimised for programmatic consumption. Together they serve the two modes of AI interaction: conversational (descriptions) and deterministic (metadata).
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 · 381 lines · 139 tokens per session scan A 9dac717d3676
metadata-schema-generator is a skill published in the GitHub repository murphytrueman/design-system-ops (176 stars, last pushed 16d ago), licensed MIT. It adds 139 tokens to every session and 3,322 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 2026-08-30.
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