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 bacchus-labs/wrangler --skill ai-component-metadatagit clone --depth 1 https://github.com/bacchus-labs/wranglerWrote 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/bacchus-labs/wrangler/ai-component-metadata)<a href="https://agentmods.dev/skills/bacchus-labs/wrangler/ai-component-metadata"><img src="https://agentmods.dev/badge/skills/bacchus-labs/wrangler/ai-component-metadata/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/bacchus-labs/wrangler/ai-component-metadata"><img src="https://agentmods.dev/badge/skills/bacchus-labs/wrangler/ai-component-metadata.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 | $0.00051 | $0.00789 |
| Opus 5 | $0.00026 | $0.00394 |
| Sonnet 5 | $0.00010 | $0.00158 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
ai-component-metadata 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
100% identical to ai-component-metadata — 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Component Metadata Generator
Generate structured, AI-consumable metadata for design system components to enable intelligent UI generation and component usage.
Quick Start
When analyzing a component, use the metadata schema template in scripts/generate_metadata.py or follow the manual process below:
# Automatic generation (reads component file)
python scripts/generate_metadata.py path/to/Component.tsx
# Or use the template directly
cp assets/metadata-template.tsx your-component-metadata.tsx
Core Workflow
1. Analyze Component Structure
Identify:
- Component composition (slots, children)
- Available variants and states
- Props and their types
- Accessibility attributes
2. Generate Metadata
Create metadata following this schema:
export const componentMetadata = {
component: {
name: "ComponentName",
category: "atoms|molecules|organisms",
description: "Brief description",
type: "interactive|display|container|input|navigation"
},
usage: {
useCases: ["primary-use", "secondary-use"],
requiredProps: [],
commonPatterns: [
{
name: "pattern-name",
description: "When to use",
composition: "JSX example"
}
],
antiPatterns: [
{
scenario: "what-not-to-do",
reason: "why",
alternative: "what-instead"
}
]
},
composition: {
slots: {},
nestedComponents: [],
commonPartners: [],
parentConstraints: []
},
behavior: {
states: [],
interactions: {},
responsive: {}
},
accessibility: {
role: "ARIA role",
keyboardSupport: "description",
screenReader: "behavior",
focusManagement: "strategy",
wcag: "AA"
},
aiHints: {
priority: "high|medium|low",
keywords: [],
context: "when to use"
}
}
3. Validate Metadata
- Test with AI generation tasks
- Verify in Storybook
- Ensure examples are runnable
Component Categories
- atoms: Basic building blocks (Button, Text, Input)
- molecules: Simple combinations (Card, Chip, FormField)
- organisms: Complex components (Header, Table, Form)
What ships with it
7 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 · 138 lines · 51 tokens per session scan A 9decb3f0a73f
ai-component-metadata is a skill published in the GitHub repository bacchus-labs/wrangler (4 stars, last pushed 6mo ago), licensed MIT. It adds 51 tokens to every session and 789 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-component-metadata, differing in 0 lines, and is treated as a copy.
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