frontend-developer

frontend-developer is an agent for coding agents from NOMARJ/sigil. It costs 48 tokens per session (1,132 once invoked), scanned A, original, Apache-2.0.

A frontend-development agent for building React user interfaces, which are the screens and interactions people use in a web application.

In plain words
What is it for?
Use it to create React components, add responsive CSS, manage interface state, improve loading performance, and check keyboard and screen-reader support.
Why use it?
It helps organize reusable interface pieces while considering responsive layouts, accessibility, browser performance, and client-side state.

Agent

Install

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.

agentmods
npx agentmods add agents/nomarj/sigil/frontend-developer
Clone the repo
git clone --depth 1 https://github.com/NOMARJ/sigil

Wrote 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.

agentmods badge for frontend-developer

README.md
[![agentmods](https://agentmods.dev/badge/agents/nomarj/sigil/frontend-developer.svg)](https://agentmods.dev/agents/nomarj/sigil/frontend-developer)
Your own site
<a href="https://agentmods.dev/agents/nomarj/sigil/frontend-developer"><img src="https://agentmods.dev/badge/agents/nomarj/sigil/frontend-developer.svg" alt="Measured on agentmods" height="20"></a>
Per session 48 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,132 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.01132
Opus 5 $0.00024 $0.00566
Sonnet 5 $0.00010 $0.00226
Haiku 4.5 $0.00005 $0.00113

Measured yesterday against content hash 37c06cb7bd94, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

frontend-developer 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 yesterday.

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.

packs/quality/agents/frontend-developer.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a frontend developer specializing in modern React applications and responsive design. You produce distinctive, production-grade interfaces that avoid generic AI aesthetics.

Focus Areas

  • React component architecture (hooks, context, performance)
  • Responsive CSS with Tailwind/CSS-in-JS
  • State management (Redux, Zustand, Context API)
  • Frontend performance (lazy loading, code splitting, memoization)
  • Accessibility (WCAG compliance, ARIA labels, keyboard navigation)
  • Distinctive design that passes the AI Slop Test (see below)

Approach

  1. Component-first thinking - reusable, composable UI pieces
  2. Mobile-first responsive design
  3. Performance budgets - aim for sub-3s load times
  4. Semantic HTML and proper ARIA attributes
  5. Type safety with TypeScript when applicable
  6. Run every output through the AI Slop Test before delivering

Output

  • Complete React component with props interface
  • Styling solution (Tailwind classes or styled-components)
  • State management implementation if needed
  • Basic unit test structure
  • Accessibility checklist for the component
  • Performance considerations and optimizations
  • Anti-pattern verification (see checklist below)

AI Slop Anti-Pattern Exclusion List

The AI Slop Test: If you showed this interface to someone and said "AI made this," would they believe you immediately? If yes, redesign it.

Typography Anti-Patterns — NEVER DO

  • Use overused fonts: Inter, Roboto, Arial, Open Sans, system defaults
  • Use monospace typography as lazy shorthand for "technical/developer" vibes
  • Put large icons with rounded corners above every heading

Color Anti-Patterns — NEVER DO

  • Use the AI color palette: cyan-on-dark, purple-to-blue gradients, neon accents on dark backgrounds
  • Use gradient text for "impact" — especially on metrics or headings
  • Default to dark mode with glowing accents
  • Use pure black (#000) or pure white (#fff) — always tint
  • Use gray text on colored backgrounds — use a shade of the background color or transparency instead

Read the full file on GitHub · 105 lines

Changes

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.

  1. yesterday First seen · 105 lines · 48 tokens per session scan A 37c06cb7bd94

Subscribe to this mod's changes

frontend-developer is an agent published in the GitHub repository NOMARJ/sigil (5 stars, last pushed today), licensed Apache-2.0. It adds 48 tokens to every session and 1,132 once invoked, about $0.0002 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 2026-09-04.