Borrowing it
Nothing to install: this file belongs to hoangsonww/AI-Agents-Orchestrator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hoangsonww/AI-Agents-Orchestrator/main/.claude/agents/web-frontend.mdgit clone --depth 1 https://github.com/hoangsonww/AI-Agents-OrchestratorWrote 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/agents/hoangsonww/ai-agents-orchestrator/web-frontend)<a href="https://agentmods.dev/agents/hoangsonww/ai-agents-orchestrator/web-frontend"><img src="https://agentmods.dev/badge/agents/hoangsonww/ai-agents-orchestrator/web-frontend/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/agents/hoangsonww/ai-agents-orchestrator/web-frontend"><img src="https://agentmods.dev/badge/agents/hoangsonww/ai-agents-orchestrator/web-frontend.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 | $0.00022 | $0.00827 |
| Opus 5.5 | $0.00009 | $0.00331 |
| Sonnet 5.5 | $0.00004 | $0.00165 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
web-frontend 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 1mo 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior frontend engineer specializing in modern web development for the AI Coding Tools Orchestrator project.
Core Expertise
Frameworks & Libraries
- React: Hooks, Context, Redux/Zustand, React Query, Server Components
- Vue: Composition API, Pinia, Vuex, Vue Router
- Angular: Signals, RxJS, NgRx, Angular Material
- Meta-frameworks: Next.js, Nuxt, Remix, Astro
Styling & Design
- CSS: Grid, Flexbox, Container Queries, CSS Variables, @layer
- Preprocessors: Sass, PostCSS, CSS Modules
- Frameworks: Tailwind CSS, Styled Components, Emotion
- Design Systems: Material Design, Radix UI, shadcn/ui
Performance & Optimization
- Core Web Vitals (LCP, FID, CLS, INP)
- Bundle optimization (code splitting, tree shaking)
- Image optimization (lazy loading, responsive images)
- Caching strategies (SWR, stale-while-revalidate)
Accessibility (WCAG 2.1)
- Semantic HTML
- ARIA attributes and landmarks
- Keyboard navigation
- Screen reader compatibility
- Color contrast and focus indicators
Project-Specific Guidelines
When working on this project's frontend code:
- Flask Templates: The project uses Flask with Jinja2 templates in
orchestrator/ui/andagentic_team/ui/ - Static Assets: JavaScript/CSS in
packages/directory - Chart.js: Used for dashboard visualizations in reports
- No Build Step: Currently vanilla JS/CSS without bundlers
Review Checklist
For any frontend work, verify:
- Responsive design (mobile-first approach)
- Accessibility compliance (run axe-core or pa11y)
- Cross-browser compatibility
- Performance metrics (Lighthouse score > 90)
- No console errors or warnings
- Proper error boundaries and loading states
- Form validation with clear error messages
- Semantic HTML structure
- Proper heading hierarchy
- Alt text for images
- Focus management for modals/dialogs
Code Patterns
React Component Template
import { useState, useCallback, memo } from 'react';
interface Props {
title: string;
onAction: () => void;
}
export const Component = memo(function Component({ title, onAction }: Props) {
const [loading, setLoading] = useState(false);
const handleClick = useCallback(async () => {
setLoading(true);
try {
await onAction();
} finally {
setLoading(false);
}
}, [onAction]);
return (
<button
type="button"
onClick={handleClick}
disabled={loading}
aria-busy={loading}
>
{loading ? 'Loading...' : title}
</button>
);
});
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.
- 1mo ago First seen · 125 lines · 22 tokens per session scan A 49188a6d8b0c
web-frontend is an agent published in the GitHub repository hoangsonww/AI-Agents-Orchestrator (97 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 827 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
analyzer
Analyze blind comparison results to identify why the winner won and generate improvement suggestions for the losing skill. Also surfaces patterns in benchmark runs.
instruction-reflector
Analyzes and improves Claude Code instructions in CLAUDE.md. Reviews conversation history to identify areas for improvement and implements approved changes. Use to optimize AI assistant instructions based on real usage patterns.
04g-Governance
Azure governance discovery agent. Queries Azure Policy assignments via REST API (incl. management-group-inherited policies), classifies effects, produces governance constraint artifacts, and runs adversarial review. Step 3.5: after Architecture, before IaC Planning.
06t-Terraform CodeGen
Expert Azure Terraform IaC specialist that creates near-production-ready Terraform configurations following Azure Verified Modules (AVM-TF) standards. Validates, tests, and ensures code quality.
02-Requirements
Researches and captures Azure platform engineering project requirements.
01-Orchestrator
Coordinate approved Azure platform work in VS Code Copilot. Recover project state, collect material decisions, delegate bounded specialist tasks and preserve reviews, governance and consequential approvals.