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messaging-ab-tester消息传递 ab 测试仪

Agent Skill

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。

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210

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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 rate30%
Reply rate40%
Positive reply rate30%
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"

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