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 Devdannnny/chisel --skill nextgit clone --depth 1 https://github.com/Devdannnny/chiselWrote 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/devdannnny/chisel/next)<a href="https://agentmods.dev/skills/devdannnny/chisel/next"><img src="https://agentmods.dev/badge/skills/devdannnny/chisel/next/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/devdannnny/chisel/next"><img src="https://agentmods.dev/badge/skills/devdannnny/chisel/next.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 | $0.00170 | $0.02105 |
| Opus 5.5 | $0.00068 | $0.00842 |
| Sonnet 5.5 | $0.00034 | $0.00421 |
| Haiku 4.5 | $0.00017 | $0.00211 |
Grade A, and why
frontend-next 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend — Next.js
Production-grade, distinctive Next.js UI. Opinionated, brand-correct, free of "AI slop" aesthetics. Built on the Prompt Architecture Standard (PAS) and the canonical design language at ../shared-rules.md.
Pre-flight (run BEFORE writing any code)
- READ
../shared-rules.mdFIRST. It is the canonical design language. This SKILL.md assumes you have those rules in context. - Check for
screenshots/in the codebase root. If present, extract typography, layout rhythm, color, spacing, atmosphere as design references. - Confirm
questionnaire.mdis answered. Walk through it if not. - Identify the router. App Router (Next 14+, preferred) vs. Pages Router (legacy). Generate matching patterns.
- Identify the surface. Landing / marketing / product UI / dashboard / portfolio — see surface-specific defaults in
../shared-rules.md. - Identify existing design system. Preserve it if present. The rules apply to greenfield work.
Role
You are a senior Next.js engineer with strong design taste. You think in Server Components first, you reach for next/font and next/image by default, and you ship distinctive, brand-correct UI on the first generation.
Objective
Produce a Next.js frontend that passes every litmus check in ../shared-rules.md — visually distinctive, brand-correct, technically sound, impossible to mistake for generic AI output — on the first generation.
Quick reference (full rules in ../shared-rules.md)
Memory aid only. Apply ALL rules from shared-rules.md:
- One composition. Brand first. Brand test.
- Distinctive Google Fonts via
next/font— NOT Inter, NOT Roboto. - No flat backgrounds. One accent. CSS variables.
- Full-bleed hero only. Hero budget: brand + headline + line + CTA + image.
- No cards by default. Cards only when card IS the interaction.
- 2–3 intentional motions (Framer Motion).
- Real visual anchor. No abstract gradient blobs.
- Both light and dark polished.
- Delete 30% of copy.
What ships with it
1 file 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.
- 1mo ago First seen · 238 lines · 170 tokens per session scan A 605c8cdc1fad
frontend-next is a skill published in the GitHub repository Devdannnny/chisel (3 stars, last pushed 3mo ago), licensed MIT. It adds 170 tokens to every session and 2,105 once invoked, about $0.0007 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 2026-08-31.
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agy-staffer
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agy-ask
Ask Google's Antigravity CLI (agy staffer, fast Gemini) a cheap one-shot question - the fast zero-tool mode and the post-install smoke test. Use when the user says /agy-ask, "ask agy", "quick second opinion from agy", or right after installing to verify the plugin works.
agy-researcher
Delegate a deep research or survey task to Google's Antigravity CLI (agy staffer, fast Gemini). Use when the user says /agy-researcher, "ask agy to research", "have the agy staffer survey X", or wants a second, independent deep-dive on a topic or codebase without spending the host model's quota.