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Agent skill

Messaging Ab Tester

by gooseworks-ai in gooseworks-ai/goose-skills

Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP.

MITAuto-check passedSales & Support

Install Messaging Ab Tester

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill messaging-ab-tester -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills messaging-ab-tester --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/brand/composites/messaging-ab-tester .claude/skills/messaging-ab-tester && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
messaging-ab-tester
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.3k tokens
SKILL.md length
877 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP.

  • Works in 5 steps: Intake → Generate Messaging Variants → Deploy Tests → …
  • A team cant decide between messaging angles and needs data
  • SKILL.md covers When to Use, Phase 0: Intake, Phase 1: Generate Messaging… and Phase 2: Deploy Tests, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Messaging Ab Tester is an agent skill from gooseworks-ai/goose-skills. Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP. Tests can run via LinkedIn organic posts, cold email subject line splits, or both. Pure reasoning for variant generation and analysis — the user deploys the tests through their own tools. Use when a team can't decide between messaging angles and needs data, not opinions.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Sales & Support, covering A/B testing, Cold outreach and Positioning and messaging. It works with LinkedIn. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • A team cant decide between messaging angles and needs data
  • Tasks that involve A/B testing
  • Tasks that involve Cold outreach

Example prompts

  • “/messaging-ab-tester”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Intake
  2. Generate Messaging Variants
  3. Deploy Tests
  4. Analyze Results
  5. Output Format

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Messaging Ab Tester loads about 2.3k tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 877 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 877 words, ~2,321 tokens.

Download SKILL.mdSave it as .claude/skills/messaging-ab-tester/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
messaging-ab-tester
description
Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP. Tests can run via LinkedIn organic posts, cold email subject line splits, or both. Pure reasoning for variant generation and analysis — the user deploys the tests through their own tools. Use when a team can't decide between messaging angles and needs data, not opinions.
tags
brand

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
  2. 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 your outreach tool (Smartlead, Instantly, Lemlist, or any tool with A/B testing):

  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)

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

Show full SKILL.md (361 more words)Show less

Phase 2B: Collect Results

After the test has run for the planned duration, gather your results:

How to provide data:

  • Paste metrics — Copy open rates, reply rates, engagement numbers directly into the chat
  • CSV export — Export campaign analytics from your outreach tool and share the file
  • Screenshot — Take a screenshot of your dashboard/analytics and share it
  • Manual input — Just tell the agent the numbers: "Variant A got 45% open rate and 3% reply rate, Variant B got 52% open rate and 5% reply rate"

For LinkedIn tests: Go to your post analytics (click "View analytics" on each post) and share impressions, reactions, comments, and profile visits per post.

For email tests: Export or screenshot your campaign's variant/A-B test results showing sends, opens, and replies per variant.

The agent will normalize whatever format you provide into the scoring framework below.

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

markdown
# 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 the current working directory or wherever the user prefers.

Cost

ComponentCost
Variant generationFree (LLM reasoning)
LinkedIn postingFree (organic)
Email testingIncluded with your outreach tool's plan
Results analysisFree (LLM reasoning)
TotalFree

Tools Required

None. Pure reasoning for variant generation, test design, and result analysis. The user deploys tests through their own tools:

  • LinkedIn organic — post variants manually or via scheduling tool
  • Cold email — set up A/B tests in whatever outreach tool they use (Smartlead, Instantly, Lemlist, etc.)
  • Results — user provides metrics (screenshots, CSV exports, or manual input) for 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"

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/brand/composites/messaging-ab-tester of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Messaging Ab Tester next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Messaging Ab Tester compared with similar skills
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Messaging Ab Tester this skillgooseworks-ai/goose-skills1.2k1 repos~2.3kAutomated safety check: PassMIT
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Atomic Messageextruct-ai/gtm-skills109—~952Automated safety check: PassNone
Icp Onboardinggrowthenginenowoslawski/coldoutboundskills753—~1.9kAutomated safety check: PassMIT
B2B Lead Generationminhnv0807/ai-business-skills609—~1.2kAutomated safety check: PassMIT
Outreach Specialistognjengt/founder-skills447—~3.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Messaging Ab Tester

What does Messaging Ab Tester do?

Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP. Messaging Ab Tester is an agent skill from gooseworks-ai/goose-skills. Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP.

When should I use Messaging Ab Tester?

Messaging Ab Tester fits situations like: A team cant decide between messaging angles and needs data; tasks that involve A/B testing; tasks that involve Cold outreach.

How do I install Messaging Ab Tester in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill messaging-ab-tester -a claude-code`. Or copy the skill folder (skills/brand/composites/messaging-ab-tester in gooseworks-ai/goose-skills) into .claude/skills/messaging-ab-tester in your project. Claude Code loads it when a task matches its description.

How do I install Messaging Ab Tester in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill messaging-ab-tester -a codex`. Or copy the skill folder (skills/brand/composites/messaging-ab-tester in gooseworks-ai/goose-skills) into .agents/skills/messaging-ab-tester in your project. Codex loads it when a task matches its description.

Can I use Messaging Ab Tester in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill messaging-ab-tester -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/messaging-ab-tester, .gemini/skills/messaging-ab-tester, .github/skills/messaging-ab-tester and .opencode/skills/messaging-ab-tester in your project.

What does Messaging Ab Tester need to run?

SKILL.md names no scripts, command-line tools or credentials: Messaging Ab Tester is instructions for the agent only.

Does Messaging Ab Tester access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Messaging Ab Tester safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Messaging Ab Tester use?

Messaging Ab Tester is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Messaging Ab Tester use?

About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Messaging Ab Tester?

Skills that share tags, products or a category with Messaging Ab Tester: Linkedin Outbound Angle (Othmane-Khadri/YALC-the-GTM-operating-system, 318 stars), Atomic Message (extruct-ai/gtm-skills, 109 stars), Icp Onboarding (growthenginenowoslawski/coldoutboundskills, 753 stars) and B2B Lead Generation (minhnv0807/ai-business-skills, 609 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Messaging Ab Tester?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.