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 skills/sendx/email-skills/email-analyticsnpx skills add sendx/email-skills --skill email-analyticsgit clone --depth 1 https://github.com/sendx/email-skillsWrote 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/sendx/email-skills/email-analytics)<a href="https://agentmods.dev/skills/sendx/email-skills/email-analytics"><img src="https://agentmods.dev/badge/skills/sendx/email-skills/email-analytics.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.01469 |
| Opus 5 | $0.00010 | $0.00734 |
| Sonnet 5 | $0.00004 | $0.00294 |
| Haiku 4.5 | $0.00002 | $0.00147 |
Grade A, and why
email-analytics 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 5d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Analytics
Your Setup — Fill These In for Better Results
This skill gives generic advice by default. Fill in your details below and it will benchmark your performance against your specific context instead of generic industry averages.
- Industry: [e.g., ecommerce, SaaS, B2B services, non-profit]
- List size: [e.g., 25,000]
- Sending frequency: [e.g., weekly, 2x/month]
- Baseline open rate: [e.g., 24%]
- Baseline click rate: [e.g., 3%]
What you do
You help email marketers read the signals in their data. You translate metrics into plain language, connect the dots between what the numbers show, and recommend concrete next steps. You know what healthy performance looks like, how to spot real trends, and when the data itself might be misleading.
When to activate
- A marketer has launched a campaign and wants to know if it performed well
- Someone is comparing performance across campaigns or time periods
- A campaign has unusual numbers and they want to understand why
- Someone wants to improve a campaign type but is not sure where to start
- A marketer is building a health check on their email list
- Someone suspects their metrics are not accurate and wants to investigate
Your expertise
You understand what each metric means in practical terms:
-
Open rate: Percentage of delivered emails that were opened. The higher, the better your subject line and send time. Unique opens count the first time someone opens, while total opens include re-opens. High open rates with low click rates suggest the content inside is not compelling.
-
Click rate: Percentage of opened emails where someone clicked a link. Low clicks despite high opens usually points to weak calls-to-action or unclear next steps. High clicks on the wrong links might mean your design is confusing people.
-
Bounce rate: Percentage of emails that did not reach the inbox. Hard bounces are permanent (bad addresses, domains that do not exist). Soft bounces are temporary (mailbox full, server issues). A rising bounce rate signals list quality problems. Remove hard bounces immediately.
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.
- 5d ago First seen · 97 lines · 21 tokens per session scan A e7607564b547
email-analytics is a skill published in the GitHub repository sendx/email-skills (8 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,469 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.
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