Email Subject Line Tester
AI-powered email subject line optimization agent — generates 10 variants, scores each on 7 open-rate predictor signals, and selects the top 3 for A/B testing.
Describe your email's topic, audience, and goal. The agent produces scored variants across multiple psychological angles (urgency, curiosity, personalization, social proof) and gives you a ready-to-launch A/B test plan with sample size guidance.
Commands
CODEBLOCK0
What Data to Provide
The agent works with:
- - Topic description — "promotional email for 30% off summer sale, audience is past buyers"
- Draft subject lines — paste your own for scoring and improvement suggestions
- Competitor examples — paste subject lines from competitor emails for pattern analysis
- Audience details — industry, demographic, relationship (subscriber, buyer, lead), engagement tier
- Email goal — promotional, transactional, re-engagement, newsletter, event invite
No integrations required. Works entirely from your descriptions.
Workspace
Creates ~/email-subjects/ containing:
- -
memory.md — saved audience profiles, brand voice notes, and past A/B test results - INLINECODE2 — past testing sessions saved as markdown (session-YYYY-MM-DD.md)
- INLINECODE3 — industry benchmark reference updated during sessions
Analysis Framework
1. The 7 Open-Rate Predictor Signals
Each subject line is scored 0-10 on each signal; total score is out of 70:
Signal 1 — Urgency Words
- - Time-limited language: "today only", "ends tonight", "last chance", "24 hours left"
- Quantity scarcity: "only 3 left", "limited spots", "while supplies last"
- Diminishes with overuse — flag if brand history shows urgency fatigue
Signal 2 — Personalization Tokens
- - Name token {{first_name}} adds 2-5% open rate lift on average
- Behavioral personalization: "You left something behind", "Based on your last order"
- Segment-specific language (buyer vs. subscriber vs. VIP)
Signal 3 — Question Format
- - Open questions create curiosity loops: "What's your biggest email mistake?"
- Yes/No questions drive agreement priming: "Ready to double your open rates?"
- Rhetorical questions require no answer — lower friction than calls to action
Signal 4 — Number Inclusion
- - Specific numbers outperform vague claims: "Save $47" beats "Save money"
- Odd numbers slightly outperform round numbers in most studies
- List-format subject lines: "5 mistakes killing your open rates"
Signal 5 — Emoji Presence
- - Single relevant emoji adds novelty in crowded inboxes; more than 2 reduces credibility
- Emoji at start of subject performs differently than at end (test both)
- Inappropriate for B2B enterprise, legal, financial contexts — flag by industry
Signal 6 — Character Length
- - Optimal range: 30-50 characters for desktop and mobile rendering
- Under 20 characters: too vague, loses context
- Over 60 characters: truncated on mobile (58% of opens are mobile)
- Preheader pairing: subject + preheader combined should tell the full story in 90 characters
Signal 7 — Power Words
- - High-engagement triggers: "exclusive", "secret", "proven", "free", "new", "you"
- Spam-trigger words to avoid: "100% free", "act now", "cash bonus", "no cost", "winner"
- Run spam filter check on every generated variant
2. Industry Benchmark Reference
| Industry | Average Open Rate | Top Quartile |
|---|
| Ecommerce | 15-20% | >25% |
| SaaS / Software |
20-25% | >32% |
| Newsletter / Media | 25-35% | >45% |
| B2B Services | 20-28% | >35% |
| Nonprofit | 26-30% | >40% |
| Healthcare | 22-27% | >35% |
3. Spam Trigger Detection
- - Scan each variant against known spam trigger word list
- Flag phrases that increase spam folder placement risk
- Check for ALL CAPS usage (more than 2 consecutive caps words triggers filters)
- Check for excessive punctuation (!!! or ???)
4. Mobile Preview Check
- - Simulate rendering at 40 characters (iPhone lock screen) and 58 characters (Gmail mobile)
- Flag subject lines that truncate at an awkward word break
- Suggest preheader text that completes the message naturally when subject is truncated
5. A/B Test Setup Guidance
- - Minimum sample size formula: n = (Z^2 × p × (1-p)) / E^2
- Z = 1.96 for 95% confidence, p = baseline open rate, E = minimum detectable effect (typically 0.02)
- Example: 25% baseline, detect 2pp lift → n = 2,401 per variant
- - Test only one variable per test (subject line only, never combine with send time changes)
- Recommended test split: 20% / 20% test, 60% winner send
- Minimum test duration: 4 hours before declaring winner (allow for time-zone spread)
Output Format
Every subject test run outputs:
- 1. 10 Scored Variants — each with total score /70, per-signal breakdown, and character count
- Top 3 Picks — recommended for A/B testing, with rationale for each selection
- Spam Flag Report — any variants with trigger words highlighted
- Mobile Preview Simulation — truncated rendering at 40 and 58 characters
- A/B Test Plan — test setup instructions with sample size recommendation
- Preheader Suggestions — paired preheader for each top-3 variant
Rules
- 1. Always generate exactly 10 variants before scoring — never fewer
- Never recommend a variant containing known spam trigger words without flagging the risk
- Score every variant on all 7 signals — no signal may be skipped
- Flag when the audience or industry context makes certain signals inappropriate (e.g., emoji in B2B financial services)
- Always include character count and mobile truncation preview for every variant
- When scoring a user-provided subject line, explain each signal score individually — not just the total
- Save session results to
~/email-subjects/history/ when the user requests INLINECODE6
邮件主题行测试工具
AI驱动的邮件主题行优化助手——生成10个变体,对每个变体在7个打开率预测信号上进行评分,并选出前3名用于A/B测试。
描述您的邮件主题、受众和目标。该助手会从多个心理角度(紧迫感、好奇心、个性化、社会认同)生成评分变体,并为您提供包含样本量指导的、可立即启动的A/B测试计划。
命令
subject test <主题> # 为某个主题生成并评分10个主题行变体
subject generate # 在更多上下文(受众、目标、语气)下生成变体
subject score <主题行> # 对您已有的特定主题行进行评分
subject ab test # 构建完整的A/B测试计划,包含样本量计算公式
subject analyze competitors # 分析您粘贴的竞争对手邮件中的主题行
subject by industry # 获取特定行业的基准数据和表现最佳的模式
subject history # 显示之前测试过的主题行及其评分
subject save # 将当前会话结果保存到 ~/email-subjects/
需要提供的数据
该助手可处理:
- - 主题描述 — 针对夏季促销30%折扣的推广邮件,受众为过往购买者
- 草稿主题行 — 粘贴您自己的主题行,用于评分和改进建议
- 竞争对手示例 — 粘贴竞争对手邮件中的主题行,用于模式分析
- 受众详情 — 行业、人口统计特征、关系(订阅者、购买者、潜在客户)、参与层级
- 邮件目标 — 推广、交易、重新激活、新闻通讯、活动邀请
无需集成。完全基于您的描述工作。
工作空间
创建 ~/email-subjects/ 目录,包含:
- - memory.md — 保存的受众画像、品牌语气笔记和过往A/B测试结果
- history/ — 过往测试会话保存为markdown文件(session-YYYY-MM-DD.md)
- benchmarks.md — 会话期间更新的行业基准参考
分析框架
1. 7个打开率预测信号
每个主题行在每个信号上评分0-10分;总分为70分:
信号1 — 紧迫感词汇
- - 限时语言:仅限今天、今晚截止、最后机会、还剩24小时
- 数量稀缺性:仅剩3件、名额有限、售完即止
- 过度使用会降低效果——如果品牌历史显示紧迫感疲劳,则标记提醒
信号2 — 个性化标记
- - 姓名标记 {{first_name}} 平均可提升2-5%的打开率
- 行为个性化:您遗忘了某样东西、根据您上次的订单
- 特定细分群体的语言(购买者 vs. 订阅者 vs. VIP)
信号3 — 问句形式
- - 开放式问题创造好奇心循环:您最大的邮件错误是什么?
- 是非问题驱动认同感铺垫:准备好让打开率翻倍了吗?
- 反问句无需回答——比行动号召的阻力更小
信号4 — 数字包含
- - 具体数字优于模糊说法:节省47美元胜过省钱
- 大多数研究中,奇数略优于偶数
- 列表格式主题行:扼杀您打开率的5个错误
信号5 — 表情符号使用
- - 单个相关表情符号在拥挤的收件箱中增加新颖感;超过2个会降低可信度
- 主题行开头的表情符号与结尾的效果不同(两者都测试)
- 不适用于B2B企业、法律、金融领域——按行业标记
信号6 — 字符长度
- - 最佳范围:30-50个字符(桌面和移动端渲染)
- 少于20个字符:过于模糊,失去上下文
- 超过60个字符:在移动端被截断(58%的打开发生在移动端)
- 预览文本搭配:主题行+预览文本组合应在90个字符内完整传达信息
信号7 — 强力词汇
- - 高参与度触发词:独家、秘密、已验证、免费、新、您
- 需避免的垃圾邮件触发词:100%免费、立即行动、现金奖励、零成本、中奖者
- 对每个生成的变体运行垃圾邮件过滤器检查
2. 行业基准参考
| 行业 | 平均打开率 | 前25%分位 |
|---|
| 电商 | 15-20% | >25% |
| SaaS/软件 |
20-25% | >32% |
| 新闻通讯/媒体 | 25-35% | >45% |
| B2B服务 | 20-28% | >35% |
| 非营利组织 | 26-30% | >40% |
| 医疗保健 | 22-27% | >35% |
3. 垃圾邮件触发检测
- - 对照已知垃圾邮件触发词列表扫描每个变体
- 标记增加进入垃圾邮件文件夹风险的短语
- 检查全大写使用(连续超过2个大写单词会触发过滤器)
- 检查过度标点符号(!!! 或 ???)
4. 移动端预览检查
- - 模拟40个字符(iPhone锁屏)和58个字符(Gmail移动端)的渲染效果
- 标记在尴尬断词处被截断的主题行
- 建议在主题行被截断时能自然补充完整信息的预览文本
5. A/B测试设置指导
- - 最小样本量公式:n = (Z² × p × (1-p)) / E²
- Z = 1.96(95%置信度),p = 基准打开率,E = 最小可检测效果(通常为0.02)
- 示例:25%基准,检测2个百分点提升 → n = 每个变体2,401个样本
- - 每次测试仅测试一个变量(仅主题行,切勿与发送时间变化结合)
- 推荐测试分配:20% / 20% 测试,60% 发送胜出版本
- 最小测试时长:宣布胜出者前至少4小时(考虑时区差异)
输出格式
每次 subject test 运行输出:
- 1. 10个评分变体 — 每个包含总分/70、各信号细分和字符数
- 前3名推荐 — 推荐用于A/B测试,附每个选择的理由
- 垃圾邮件标记报告 — 任何包含触发词的变体高亮显示
- 移动端预览模拟 — 在40和58个字符处的截断渲染效果
- A/B测试计划 — 测试设置说明及样本量建议
- 预览文本建议 — 为前3名变体配对的预览文本
规则
- 1. 评分前始终生成恰好10个变体——不能少于10个
- 绝不推荐包含已知垃圾邮件触发词的变体而不标记风险
- 对所有7个信号进行每个变体的评分——不得跳过任何信号
- 当受众或行业背景使某些信号不适用时进行标记(例如,B2B金融服务中的表情符号)
- 始终包含每个变体的字符数和移动端截断预览
- 对用户提供的主题行进行评分时,逐一解释每个信号的评分——而不仅仅是总分
- 当用户请求 subject save 时,将会话结果保存到 ~/email-subjects/history/