Agentset is an open-source platform for building retrieval-augmented generation applications, which let AI systems find relevant information from document collections before generating responses. Developers use it for ingestion, vector search, evaluation, cited chat, hosting, and agentic applications through APIs and SDKs. Its catalogue rules and skills provide agent guidance for working with Agentset.
Borrowing it
Nothing to install: this file belongs to agentset-ai/agentset. 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/agentset-ai/agentset/main/.agents/skills/react-email/SKILL.mdgit clone --depth 1 https://github.com/agentset-ai/agentsetWrote 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/agentset-ai/agentset/react-email)<a href="https://agentmods.dev/skills/agentset-ai/agentset/react-email"><img src="https://agentmods.dev/badge/skills/agentset-ai/agentset/react-email/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/agentset-ai/agentset/react-email"><img src="https://agentmods.dev/badge/skills/agentset-ai/agentset/react-email.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 17 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00057 | $0.03382 |
| Opus 5.5 | $0.00023 | $0.01353 |
| Sonnet 5.5 | $0.00011 | $0.00676 |
| Haiku 4.5 | $0.00006 | $0.00338 |
Grade A, and why
react-email 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 — 457 lines — stays where its author put it; the contents beside it link to each section on GitHub.
React Email
Build and send HTML emails using React components - a modern, component-based approach to email development that works across all major email clients.
Installation
New Project
Scaffold a new React Email project:
npx create-email@latest
cd react-email-starter
npm install
npm run dev
This creates a react-email-starter folder with sample templates. Also works with yarn, pnpm, or bun.
Existing Project
Add React Email to an existing codebase:
- Install dependencies:
npm install @react-email/components
npm install react-email @react-email/preview-server -D
- Create an emails directory:
mkdir emails
- Add a preview script to package.json:
{
"scripts": {
"email:dev": "email dev"
}
}
- Start the preview server:
npm run email:dev
The --dir flag specifies where email templates are stored. Adjust the path to match your project structure (e.g., src/emails, app/emails).
Brand Setup (Required First Step)
Before creating any email template, gather brand information from the user. This ensures visual consistency across all emails.
Required Brand Information
Ask the user for:
-
Brand colors
- Primary color (main brand color for buttons, links, accents)
- Secondary color (supporting color for backgrounds, borders)
- Text color (default:
#1a1a1afor light mode) - Background color (default:
#f4f4f5for light mode)
-
Logo
- Logo image URL (must be absolute URL, PNG or JPEG)
- Logo dimensions or aspect ratio preference
- Logo placement preference (centered, left-aligned)
- Ask user for file and path of the local file if the logo is not a publicly accessible URL.
-
Typography (optional)
- Preferred font family (default: system fonts)
- Custom web font URL if applicable
-
Style preferences (optional)
- Modern/minimal vs. classic/traditional
- Rounded corners vs. sharp edges
- Spacing density (compact vs. spacious)
- Never add emojis unless the user explicitly requests them.
What ships with it
5 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.
- 1mo ago First seen · 457 lines · 57 tokens per session scan A c1b359291f9b
react-email is a skill published in the GitHub repository agentset-ai/agentset (2,112 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 3,382 once invoked, about $0.0002 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.
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