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 agentmods add agents/advaitgore/arbor/frontendgit clone --depth 1 https://github.com/advaitgore/ArborWhat 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 | $0.00014 | $0.01134 |
| Opus 5 | $0.00007 | $0.00567 |
| Sonnet 5 | $0.00003 | $0.00227 |
| Haiku 4.5 | $0.00001 | $0.00113 |
Grade A, and why
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 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend Design Instructions for GitHub Copilot
Use these instructions whenever generating or editing frontend code, including React components, web pages, landing pages, dashboards, design systems, HTML/CSS layouts, or UI styling work.
Goal
Create distinctive, production-grade frontend interfaces with high design quality. The output should feel intentionally designed, not generic or template-like.
Design thinking
Before writing code, infer and commit to a clear aesthetic direction.
- Purpose: Understand what the interface does and who it serves.
- Tone: Pick a strong direction and execute it consistently; examples include editorial, brutalist, luxury, playful, industrial, retro-futurist, soft, minimal, geometric, raw, or organic.
- Constraints: Respect framework, performance, accessibility, responsiveness, and maintainability constraints.
- Differentiation: Make one memorable design choice that gives the interface character.
Important: intentionality matters more than visual intensity. Minimal interfaces should feel precise and refined. Maximal interfaces should feel cohesive and deliberate.
Output standards
Generate code that is:
- Production-ready and functional.
- Visually polished and memorable.
- Cohesive, with a clear point of view.
- Refined in typography, spacing, states, and hierarchy.
- Responsive by default.
- Accessible by default.
Typography
- Prefer distinctive font choices over default ones.
- Avoid generic defaults such as Arial, Roboto, and system-font-only styling unless the project explicitly calls for them.
- Avoid overusing Inter or Space Grotesk as the automatic choice.
- Pair a characterful display font with a readable body font when the design supports it.
- Use font hierarchy intentionally: display, section heading, body, label.
- Use consistent spacing, line height, and letter spacing.
Color and theme
- Commit to a cohesive palette with a clear hierarchy.
- Use CSS variables or design tokens for colors, spacing, radius, and shadows.
- Prefer a dominant base with restrained accent colors.
- Light or dark mode are both acceptable; choose the one that better fits the concept.
- Avoid cliché AI aesthetics, especially purple-gradient-on-white SaaS styling unless explicitly requested.
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.
- yesterday First seen · 131 lines · 14 tokens per session scan A 346cce74cf1a
frontend is an agent published in the GitHub repository advaitgore/Arbor (0 stars, last pushed 2mo ago), licensed MIT. It adds 14 tokens to every session and 1,134 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.