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 travisjneuman/.claude --skill email-systemsgit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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/travisjneuman/.claude/email-systems)<a href="https://agentmods.dev/skills/travisjneuman/.claude/email-systems"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/email-systems/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/travisjneuman/.claude/email-systems"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/email-systems.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.04268 |
| Opus 5.5 | $0.00022 | $0.01707 |
| Sonnet 5.5 | $0.00011 | $0.00854 |
| Haiku 4.5 | $0.00006 | $0.00427 |
Grade A, and why
email-systems 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 20d 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 — 501 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Systems
Overview
This skill covers building reliable email systems for web applications, including transactional email sending, template design, deliverability optimization, and inbox placement. It addresses integration with email service providers (Resend, AWS SES, SendGrid, Postmark), building responsive HTML email templates with React Email and MJML, configuring DNS records for deliverability (SPF, DKIM, DMARC), managing email types (transactional, notification, marketing), queue management, and bounce/complaint handling.
Use this skill when sending welcome emails, password resets, order confirmations, notification digests, marketing campaigns, or any system-generated email. Also use when debugging deliverability issues or migrating between email providers.
Core Principles
- Transactional and marketing are different - Transactional emails (password reset, receipt) must arrive instantly and reliably. Marketing emails (newsletter, promo) can be batched and should have unsubscribe links. Never mix them on the same sending domain.
- HTML email is not web development - Email clients render HTML from 2004. No flexbox, no grid, no modern CSS. Use tables for layout, inline styles, and test across clients. React Email and MJML abstract this pain.
- Deliverability is earned - SPF, DKIM, and DMARC are table stakes, not guarantees. Maintain low bounce rates (< 2%), low complaint rates (< 0.1%), and warm up new sending domains gradually.
- Queue everything - Never send email synchronously in a request handler. Queue email sends to avoid blocking user requests and to enable retry on failure.
- Test before sending - Use Mailtrap or Ethereal in development, preview in multiple clients (Litmus, Email on Acid), and verify every link works.
Key Patterns
Pattern 1: React Email Templates with Resend
When to use: Building type-safe, component-based email templates with modern DX and reliable delivery.
Implementation:
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.
- 20d ago Changed · +1 lines 6d836c45c516
- 1mo ago First seen · 500 lines · 55 tokens per session scan A f722459c0b71
email-systems is a skill published in the GitHub repository travisjneuman/.claude (106 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 4,268 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 2026-09-03.
Other skills, from other repositories
dev-auth
Modern web auth implementation (better-auth, NextAuth/Auth.js, Clerk, Supabase Auth; migrating off the deprecated Lucia). Trigger when the user wants to add login, signup, sessions, OAuth, magic links, 2FA, or when existing auth code is detected to audit or migrate.
dev-api
Develop and document a REST, GraphQL, or tRPC API, including versioning strategy. Use when the user wants to create an endpoint, a route, a type-safe procedure, or structure/version an API.
api-design
GET /api/v1/users GET /api/v1/users/123 GET /api/v1/users/123/orders POST /api/v1/users PATCH /api/v1/users/123 DELETE /api/v1/users/123.
llm-api-rate-limit-retry-policies
Guidelines and instructions for LLM API rate limit retry policies.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.