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Agent Skill · 开发

messaging-ab-tester(消息传递 ab 测试仪)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

messaging-ab-tester 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 20,882。

总安装 210周安装 9GitHub Stars 607下载 73核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:messaging-ab-tester(消息传递 ab 测试仪)
来源仓库:https://github.com/athina-ai/goose-skills
仓库路径:skills/messaging-ab-tester
安装命令:
npx skills add https://github.com/athina-ai/goose-skills --skill messaging-ab-tester
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/athina-ai/goose-skills --skill messaging-ab-tester

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

在真实渠道测试不同价值主张表述效果,衡量哪种话术更受目标人群欢迎。特别适用于流量较小的初创企业,通过 LinkedIn 曝光与冷邮件数据进行快速迭代。生成多个变体并分配至不同受众组,收集回复率与互动指标。涉及外部平台操作,请区分测试环境与生产环境的账号隔离措施。

messaging-ab-tester 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/messaging-ab-tester

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/athina-ai/goose-skills --skill messaging-ab-te…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

messaging-ab-tester 怎么安装?

在终端执行 npx skills add https://github.com/athina-ai/goose-skills --skill messaging-ab-tester。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

messaging-ab-tester 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

messaging-ab-tester 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

messaging-ab-tester 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

messaging-ab-tester 用的人多吗?

公开数据是累计安装 210 次,近一周安装 9 次,下载 73 次,GitHub 607 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

messaging-ab-tester 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

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6,636,960576只读MIT-0通过
entra-app-registration

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6,631,872764接触敏感数据未明确通过
entra-app-registration

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3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
messaging-ab-tester

消息传递 ab 测试仪 · 本页

210607只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Messaging A/B Tester

Stop debating which message is better — test it. Generate messaging variants, deploy them through real channels, and measure which framing actually resonates with your ICP.

Core principle: At seed/Series A, you don't have enough traffic for website A/B tests. But you do have enough LinkedIn impressions and cold email sends to test messaging angles fast.

When to Use

  • "Which of these value props should we lead with?"
  • "Test our messaging angles and tell me which works"
  • "I can't decide between [message A] and [message B]"
  • "What messaging resonates most with [ICP]?"
  • "Run a messaging test for [product/feature]"

Phase 0: Intake

What to Test

  1. Core value prop — The claim or positioning you want to test (e.g., "We help growth teams run outbound 10x faster")
  2. Test goal — What are you deciding? (Headline for website, cold email angle, LinkedIn content strategy, ad copy direction)
  3. ICP — Who should this resonate with? (Title, company type, stage)
  4. Current messaging — What are you using today? (Baseline to beat)

Test Channel

  1. Where to test:

- LinkedIn organic — Post variants across consecutive days, compare engagement - Cold email — A/B test subject lines or opening hooks via Smartlead - Both — Run in parallel for fastest signal

  1. Sample size available:

- LinkedIn: followers/typical impressions per post - Email: list size available for testing

Constraints

  1. Number of variants — 3-5 recommended (more = slower signal)
  2. Test duration — How long to run? (Default: 1 week for LinkedIn, 3-5 days for email)

Phase 1: Generate Messaging Variants

Create 3-5 variants that test different angles, not just different words. Each variant should represent a distinct strategic bet:

Variant Types

TypeWhat It TestsExample
Outcome-drivenLeading with the result"3x your pipeline in 30 days"
Pain-drivenLeading with the problem"Tired of spending 4 hours a day on manual prospecting?"
Identity-drivenLeading with who they are"Built for growth teams who move fast"
Proof-drivenLeading with evidence"How [Customer] went from 10 to 50 demos/month"
Contrast-drivenLeading with what you're not"Not another CRM. An outbound engine."

Variant Template

For each variant:

VARIANT [N]: [Type — e.g., "Outcome-driven"]

Hypothesis: This framing will resonate because [reasoning tied to ICP psychology]

LinkedIn post version:
---
[Full post copy — 100-200 words, native LinkedIn format]
---

Email subject line version:
[Subject line — max 50 chars]

Email opening hook version:
[First 2 sentences of an email]

Headline version:
[Website headline — max 10 words]

Phase 2: Deploy Tests

Option A: LinkedIn Organic Test

Setup:

  1. Schedule variants as consecutive posts (1 per day, same time of day)
  2. Each post should be similar length and format (control for post structure)
  3. Don't boost any posts — organic only for clean comparison

Measurement (after 48 hours per post):

  • Impressions
  • Reactions (likes, celebrates, etc.)
  • Comments
  • Comment sentiment (positive/negative/neutral)
  • Profile visits (if trackable)
  • DMs received mentioning the post

Option B: Cold Email A/B Test

Setup via Smartlead:

  1. Create campaign with all variants as A/B test sequences
  2. Split list evenly across variants (minimum 50 per variant for signal)
  3. Same send time, same sender, same CTA — only the messaging changes

Measurement (after 5 days):

  • Open rate (tests subject line)
  • Reply rate (tests full message resonance)
  • Positive reply rate (tests conversion quality)
  • Click rate (if link included)

Option C: Both (Recommended)

Run LinkedIn and email in parallel. Different channels may show different winners — that's valuable signal about where each message works best.

Phase 3: Analyze Results

Scoring Framework

MetricWeight (LinkedIn)Weight (Email)
Engagement rate30%—
Comment quality30%—
Open rate—30%
Reply rate—40%
Positive reply rate—30%
Impressions20%—
Profile visits / clicks20%—

Statistical Significance Check

For email tests:

  • Minimum sends per variant: 50 (for directional signal), 200+ (for confident decisions)
  • Minimum difference to call a winner: >20% relative difference in primary metric

For LinkedIn tests:

  • Minimum posts per variant: 1 (you're testing with limited data — treat as directional)
  • Minimum impressions: 500 per post to be comparable

Winner Selection

WINNER: Variant [N] — [Type]

Primary metric: [X] (vs average of [Y] across other variants)
Relative improvement: [Z%] over baseline

Why it won:
[1-2 sentences on what this tells us about ICP messaging preferences]

Runner-up: Variant [N]
[1 sentence on when this might work better — different channel, different segment]

Phase 4: Output Format

# Messaging A/B Test Results — [DATE]
Value prop tested: [description]
ICP: [target audience]
Test duration: [dates]

---

## Test Design

| Variant | Type | Hypothesis |
|---------|------|-----------|
| A | [Type] | [Hypothesis] |
| B | [Type] | [Hypothesis] |
| C | [Type] | [Hypothesis] |

---

## Results

### LinkedIn Test

| Variant | Impressions | Reactions | Comments | Engagement Rate | Score |
|---------|------------|-----------|----------|----------------|-------|
| A | [N] | [N] | [N] | [X%] | [weighted] |
| B | [N] | [N] | [N] | [X%] | [weighted] |
| C | [N] | [N] | [N] | [X%] | [weighted] |

### Email Test

| Variant | Sends | Opens | Open Rate | Replies | Reply Rate | Positive | Score |
|---------|-------|-------|-----------|---------|------------|----------|-------|
| A | [N] | [N] | [X%] | [N] | [X%] | [N] | [weighted] |
| B | [N] | [N] | [X%] | [N] | [X%] | [N] | [weighted] |
| C | [N] | [N] | [X%] | [N] | [X%] | [N] | [weighted] |

---

## Winner: Variant [N] — "[Headline]"

**Why it won:** [Analysis — what does this tell us about how our ICP thinks?]

**Recommended deployment:**
- Website headline: "[adapted version]"
- Sales deck opening: "[adapted version]"
- LinkedIn bio: "[adapted version]"
- Cold email default: "[adapted version]"

---

## Variant Details & Copy

### Variant A: [Full copy used in test]
### Variant B: [Full copy used in test]
### Variant C: [Full copy used in test]

---

## What to Test Next

Based on these results, the next messaging test should explore:
1. [Angle suggested by results — e.g., "test more specific proof points since proof-driven won"]
2. [Segment test — e.g., "test winning message against different ICP segment"]

Save to clients/<client-name>/product-marketing/messaging-tests/[test-slug]-[YYYY-MM-DD].md.

Cost

ComponentCost
Variant generationFree (LLM reasoning)
LinkedIn postingFree (organic)
Email testing (via Smartlead)Included in Smartlead plan
Results analysisFree (LLM reasoning)
TotalFree (assuming existing Smartlead subscription)

Tools Required

  • Smartlead — for email A/B testing (optional — only if testing via email)
  • No other tools required for LinkedIn organic testing
  • Pure reasoning for variant generation and analysis

Trigger Phrases

  • "Test which messaging angle works best for [ICP]"
  • "Run a messaging A/B test for [value prop]"
  • "Which of these messages should we lead with?"
  • "Help me decide between these positioning options"

平台分布

Codex

37.26%
按下载量换算27

Claude

29.63%
按下载量换算22

Cursor

17.47%
按下载量换算13

Gemini CLI

9.69%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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