ab-test-generator: Finance & Market Analysis for AI Agents | OpenAgentSkill

Overview

Generate A/B test variants for affiliate content. Triggers on: "create A/B test", "test my headline", "optimize my CTA", "generate variants", "split test ideas", "improve click-through rate", "test my landing page copy", "headline alternatives", "CTA variations", "which version is better", "optimize conversions", "test my email subject line", "compare approaches".

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A/B Test Generator

Generate A/B test variants for affiliate content — headlines, CTAs, landing page sections, email subject lines, and social post hooks. Each variant includes a hypothesis explaining why it might outperform the original. Output is a Markdown document with the original, variants, hypotheses, and a test plan.

Stage

S6: Analytics — Small changes in headlines and CTAs can swing conversion rates by 20-50%. A/B testing is how professional affiliates systematically find what converts best. This skill removes the guesswork by generating theory-driven variants using proven copywriting frameworks.

When to Use

User wants to improve conversion rates on existing content
User has a headline, CTA, or email subject line and wants alternatives
User says "test my headline", "optimize my CTA", "A/B test ideas"
User has a landing page section that isn't converting
User wants to compare different messaging approaches
Chaining from S2-S5: take any content output and generate test variants

Input Schema

original: string               # REQUIRED — the content to test (headline, CTA, paragraph,
                               # email subject line, or full social post)

content_type: string           # REQUIRED — "headline" | "cta" | "landing_section"
                               # | "email_subject" | "social_hook"

goal: string                   # OPTIONAL — "clicks" | "signups" | "purchases"
                               # Default: "clicks"

num_variants: number           # OPTIONAL — number of variants to generate (2-5)
                               # Default: 3

audience: string               # OPTIONAL — who sees this content
                               # (e.g., "SaaS founders", "content creators")

product: string                # OPTIONAL — product being promoted

Chaining context: If S2-S5 content exists in conversation, the user can reference it: "test the headline from my blog post" or "generate CTA variants for my landing page."

Workflow

Step 1: Analyze Original Content

Break down the original into components:

Emotional angle: What emotion does it trigger? (curiosity, fear, desire, urgency)
Specificity: How specific vs vague?
Structure: Question, statement, command, statistic?
Framework: Which copywriting framework does it follow? (PAS, AIDA, 4U, BAB)

Step 2: Identify Testable Elements

Determine what to vary:

Emotional angle (switch from curiosity to urgency)
Specificity (add numbers, remove vagueness)
Structure (question vs statement)
Length (shorter vs longer)
Power words (swap key words for stronger alternatives)
Social proof (add or remove)

Step 3: Generate Variants

Create num_variants alternatives, each using a different approach:

Variant A: Different emotional angle
Variant B: Different structure/format
Variant C: Different specificity level
Additional variants explore social proof, urgency, or contrarian angles

Each variant must:

Preserve the core message and product reference
Preserve any FTC disclosure from the original
Be a realistic alternative (not just a word swap)

Step 4: Write Hypotheses

For each variant, explain:

What was changed and why
Which copywriting principle supports the change
What behavior change is expected (e.g., "Higher CTR because questions create open loops")

Step 5: Suggest Test Plan

Recommend:

Sample size needed (minimum 100 impressions per variant for social, 500 for landing pages)
Test duration (7-14 days minimum)
What metric to track (CTR, conversion rate, revenue per visitor)
When to declare a winner (95% statistical significance or practical significance threshold)

Step 6: Self-Validation

Before presenting output, verify:

3-5 distinct variants generated (not just word swaps)
Each hypothesis grounded in a copywriting principle or framework
Sample size calculation is present and realistic
Test duration is ≥7 days minimum
Winner criteria defined with statistical significance threshold

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

Output Schema

output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
test:
  original: string
  content_type: string
  goal: string

variants:
  - label: string              # "Variant A", "Variant B", etc.
    content: string            # the variant text
    change: string             # what was changed
    framework: string          # copywriting principle used
    hypothesis: string         # why this might win

test_plan:
  sample_size: number          # per variant
  duration: string             # recommended test period
  metric: string               # what to measure
  winner_criteria: string      # when to pick a winner

Output Format

Original — the current content being tested
Variants — each variant with its content, change description, and hypothesis
Test Plan — sample size, duration, metric, winner criteria
Quick Win — if one variant is clearly stronger based on copywriting principles, call it out

Error Handling

Original too short (1-2 words): "I need more context. Paste the full headline, CTA, or email subject line you want to test."
Content type unclear: "Is this a headline, CTA button text, email subject line, or social post hook? Knowing the format helps me generate better variants."
Too many variants requested (>5): "I'll generate 5 high-quality variants. More than 5 makes testing impractical — you'd need a very large audience to reach statistical significance."

Examples

Example 1: Blog headline test

User: "Test this headline: 'HeyGen Review: Is It Worth It in 2026?'" Action: Generate 3 variants. Variant A: "I Tested HeyGen for 30 Days — Here's What Happened" (curiosity + personal experience). Variant B: "HeyGen vs Synthesia: Which AI Video Tool Wins?" (comparison + specificity). Variant C: "The AI Video Tool That Cut My Production Time by 80%" (result + specificity). Each with hypothesis.

Example 2: CTA button test

User: "Optimize this CTA: 'Start Free Trial'" Action: Variant A: "Try HeyGen Free — No Card Required" (reduces friction). Variant B: "Create Your First AI Video in 2 Minutes" (outcome-focused). Variant C: "Get Started Free →" (shorter, action-oriented). Test plan: minimum 500 clicks per variant, track conversion rate.

Example 3: Email subject line test

User: "I'm sending an email about Semrush. Test this subject: 'Check out Semrush — it's great for SEO'" Action: Identify weakness (vague, no hook). Variant A: "The SEO tool I use to rank #1 (not kidding)" (social proof + curiosity). Variant B: "Your competitors are using this — are you?" (FOMO). Variant C: "3 Semrush features that doubled my organic traffic" (specificity + result). Each preserves FTC compliance.

References

shared/references/ftc-compliance.md — Ensure variants preserve FTC disclosure from original. Referenced in Step 3.
shared/references/flywheel-connections.md — master flywheel connection map

Flywheel Connections

Feeds Into

purple-cow-audit (S1) — winning variants reveal what resonates = what's remarkable
performance-report (S6) — test results for reporting

Fed By

viral-post-writer (S2) — posts to test variations of
twitter-thread-writer (S2) — thread hooks to test
landing-page-creator (S4) — landing page elements to test
content-pillar-atomizer (S2) — volume mode variants for testing

Feedback Loop

Test results directly improve all content-producing skills → winning headlines, CTAs, and angles feed into next content creation cycle

chain_metadata:
  skill_slug: "ab-test-generator"
  stage: "analytics"
  timestamp: string
  suggested_next:
    - "performance-report"
    - "viral-post-writer"
    - "landing-page-creator"

View source ↗

File metadata

name: ab-test-generator
description: >
  Generate A/B test variants for affiliate content. Triggers on:
  "create A/B test", "test my headline", "optimize my CTA", "generate variants",
  "split test ideas", "improve click-through rate", "test my landing page copy",
  "headline alternatives", "CTA variations", "which version is better",
  "optimize conversions", "test my email subject line", "compare approaches".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "analytics", "optimization", "tracking", "ab-testing", "experiments"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S6-Analytics

View original text

---
name: ab-test-generator
description: >
  Generate A/B test variants for affiliate content. Triggers on:
  "create A/B test", "test my headline", "optimize my CTA", "generate variants",
  "split test ideas", "improve click-through rate", "test my landing page copy",
  "headline alternatives", "CTA variations", "which version is better",
  "optimize conversions", "test my email subject line", "compare approaches".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "analytics", "optimization", "tracking", "ab-testing", "experiments"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S6-Analytics
---

# A/B Test Generator

Generate A/B test variants for affiliate content — headlines, CTAs, landing page sections, email subject lines, and social post hooks. Each variant includes a hypothesis explaining why it might outperform the original. Output is a Markdown document with the original, variants, hypotheses, and a test plan.

## Stage

S6: Analytics — Small changes in headlines and CTAs can swing conversion rates by 20-50%. A/B testing is how professional affiliates systematically find what converts best. This skill removes the guesswork by generating theory-driven variants using proven copywriting frameworks.

## When to Use

- User wants to improve conversion rates on existing content
- User has a headline, CTA, or email subject line and wants alternatives
- User says "test my headline", "optimize my CTA", "A/B test ideas"
- User has a landing page section that isn't converting
- User wants to compare different messaging approaches
- Chaining from S2-S5: take any content output and generate test variants

## Input Schema

```yaml
original: string               # REQUIRED — the content to test (headline, CTA, paragraph,
                               # email subject line, or full social post)

content_type: string           # REQUIRED — "headline" | "cta" | "landing_section"
                               # | "email_subject" | "social_hook"

goal: string                   # OPTIONAL — "clicks" | "signups" | "purchases"
                               # Default: "clicks"

num_variants: number           # OPTIONAL — number of variants to generate (2-5)
                               # Default: 3

audience: string               # OPTIONAL — who sees this content
                               # (e.g., "SaaS founders", "content creators")

product: string                # OPTIONAL — product being promoted
```

**Chaining context**: If S2-S5 content exists in conversation, the user can reference it: "test the headline from my blog post" or "generate CTA variants for my landing page."

## Workflow

### Step 1: Analyze Original Content

Break down the original into components:
- **Emotional angle**: What emotion does it trigger? (curiosity, fear, desire, urgency)
- **Specificity**: How specific vs vague?
- **Structure**: Question, statement, command, statistic?
- **Framework**: Which copywriting framework does it follow? (PAS, AIDA, 4U, BAB)

### Step 2: Identify Testable Elements

Determine what to vary:
- Emotional angle (switch from curiosity to urgency)
- Specificity (add numbers, remove vagueness)
- Structure (question vs statement)
- Length (shorter vs longer)
- Power words (swap key words for stronger alternatives)
- Social proof (add or remove)

### Step 3: Generate Variants

Create `num_variants` alternatives, each using a different approach:
- **Variant A**: Different emotional angle
- **Variant B**: Different structure/format
- **Variant C**: Different specificity level
- Additional variants explore social proof, urgency, or contrarian angles

Each variant must:
- Preserve the core message and product reference
- Preserve any FTC disclosure from the original
- Be a realistic alternative (not just a word swap)

### Step 4: Write Hypotheses

For each variant, explain:
- What was changed and why
- Which copywriting principle supports the change
- What behavior change is expected (e.g., "Higher CTR because questions create open loops")

### Step 5: Suggest Test Plan

Recommend:
- Sample size needed (minimum 100 impressions per variant for social, 500 for landing pages)
- Test duration (7-14 days minimum)
- What metric to track (CTR, conversion rate, revenue per visitor)
- When to declare a winner (95% statistical significance or practical significance threshold)

### Step 6: Self-Validation

Before presenting output, verify:

- [ ] 3-5 distinct variants generated (not just word swaps)
- [ ] Each hypothesis grounded in a copywriting principle or framework
- [ ] Sample size calculation is present and realistic
- [ ] Test duration is ≥7 days minimum
- [ ] Winner criteria defined with statistical significance threshold

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

## Output Schema

```yaml
output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
test:
  original: string
  content_type: string
  goal: string

variants:
  - label: string              # "Variant A", "Variant B", etc.
    content: string            # the variant text
    change: string             # what was changed
    framework: string          # copywriting principle used
    hypothesis: string         # why this might win

test_plan:
  sample_size: number          # per variant
  duration: string             # recommended test period
  metric: string               # what to measure
  winner_criteria: string      # when to pick a winner
```

## Output Format

1. **Original** — the current content being tested
2. **Variants** — each variant with its content, change description, and hypothesis
3. **Test Plan** — sample size, duration, metric, winner criteria
4. **Quick Win** — if one variant is clearly stronger based on copywriting principles, call it out

## Error Handling

- **Original too short (1-2 words)**: "I need more context. Paste the full headline, CTA, or email subject line you want to test."
- **Content type unclear**: "Is this a headline, CTA button text, email subject line, or social post hook? Knowing the format helps me generate better variants."
- **Too many variants requested (>5)**: "I'll generate 5 high-quality variants. More than 5 makes testing impractical — you'd need a very large audience to reach statistical significance."

## Examples

### Example 1: Blog headline test

**User**: "Test this headline: 'HeyGen Review: Is It Worth It in 2026?'"
**Action**: Generate 3 variants. Variant A: "I Tested HeyGen for 30 Days — Here's What Happened" (curiosity + personal experience). Variant B: "HeyGen vs Synthesia: Which AI Video Tool Wins?" (comparison + specificity). Variant C: "The AI Video Tool That Cut My Production Time by 80%" (result + specificity). Each with hypothesis.

### Example 2: CTA button test

**User**: "Optimize this CTA: 'Start Free Trial'"
**Action**: Variant A: "Try HeyGen Free — No Card Required" (reduces friction). Variant B: "Create Your First AI Video in 2 Minutes" (outcome-focused). Variant C: "Get Started Free →" (shorter, action-oriented). Test plan: minimum 500 clicks per variant, track conversion rate.

### Example 3: Email subject line test

**User**: "I'm sending an email about Semrush. Test this subject: 'Check out Semrush — it's great for SEO'"
**Action**: Identify weakness (vague, no hook). Variant A: "The SEO tool I use to rank #1 (not kidding)" (social proof + curiosity). Variant B: "Your competitors are using this — are you?" (FOMO). Variant C: "3 Semrush features that doubled my organic traffic" (specificity + result). Each preserves FTC compliance.

## References

- `shared/references/ftc-compliance.md` — Ensure variants preserve FTC disclosure from original. Referenced in Step 3.
- `shared/references/flywheel-connections.md` — master flywheel connection map

## Flywheel Connections

### Feeds Into
- `purple-cow-audit` (S1) — winning variants reveal what resonates = what's remarkable
- `performance-report` (S6) — test results for reporting

### Fed By
- `viral-post-writer` (S2) — posts to test variations of
- `twitter-thread-writer` (S2) — thread hooks to test
- `landing-page-creator` (S4) — landing page elements to test
- `content-pillar-atomizer` (S2) — volume mode variants for testing

### Feedback Loop
- Test results directly improve all content-producing skills → winning headlines, CTAs, and angles feed into next content creation cycle

```yaml
chain_metadata:
  skill_slug: "ab-test-generator"
  stage: "analytics"
  timestamp: string
  suggested_next:
    - "performance-report"
    - "viral-post-writer"
    - "landing-page-creator"
```

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: shell or command execution, filesystem or document access
Permission surface: shell or command execution, filesystem or document access

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3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: Affitor/affiliate-skills
License: MIT
Version: 1.0.0
Last GitHub push: Jun 14, 2026
Registry updated: Sep 3, 2026
Instruction path: skills/analytics/ab-test-generator/SKILL.md @ ed17ef37bc16

Version reported in registry metadata; check source releases before relying on it.

Quality

73/100

Strong

Trust

70/100

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This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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    "url": "https://www.openagentskill.com/skills/affitor-ab-test-generator",
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      "permissionSurface": "shell or command execution, filesystem or document access",
      "documentation": "Strong README/SKILL.md context",
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      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 79,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Permission surface may require sandboxing",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: shell or command execution, filesystem or document access",
      "Permission surface: shell or command execution, filesystem or document access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 73,
    "label": "Strong"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Testing and QA",
    "maintenance": "3mo since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution",
    "Permission surface may require sandboxing",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
    "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval."
  ],
  "agent_contract": {
    "task_input": "Use ab-test-generator in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 78/100 Strong shortlist",
      "Audit: 79/100 Risky",
      "Safety: 47/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "affitor-ab-test-generator (ab-test-generator)",
      "install_command": "npx skills add Affitor/affiliate-skills --skill ab-test-generator",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "affitor-ab-test-generator",
      "task": "Use ab-test-generator in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/affitor-ab-test-generator",
    "api": "https://www.openagentskill.com/api/agent/skills/affitor-ab-test-generator",
    "audit": "https://www.openagentskill.com/skills/affitor-ab-test-generator/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=affitor-ab-test-generator&task=Use%20ab-test-generator%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ab-test-generator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ab-test-generator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/affitor-ab-test-generator/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/affitor-ab-test-generator"
  }
}

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Creator: Affitor
Source: Affitor/affiliate-skills
Indexed by: OpenAgentSkill community index

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