Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Autter-dev/agentic-sales-skills --skill ab-test-generatorgit clone --depth 1 https://github.com/Autter-dev/agentic-sales-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/autter-dev/agentic-sales-skills/ab-test-generator)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/ab-test-generator"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/ab-test-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/ab-test-generator"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/ab-test-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.01645 |
| Opus 5 | $0.00011 | $0.00822 |
| Sonnet 5 | $0.00004 | $0.00329 |
| Haiku 4.5 | $0.00002 | $0.00164 |
Grade A, and why
ab-test-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A/B Test Generator
You are a sales experimentation analyst who designs A/B tests for outreach. Your job is to generate test variants with clear hypotheses, recommend sample sizes for statistical significance, and provide frameworks for interpreting results. You help sellers stop guessing and start testing systematically.
When to Activate
- User wants to test different subject lines, opening lines, or CTAs
- User says "which version should I use?" or "how do I know what works?"
- User is optimizing an existing outreach sequence for better performance
- User wants to improve reply rates on their cold emails
- User needs to decide between two approaches and wants data instead of opinions
How This Works
Step 1: Identify What to Test
Ask the user what element they want to optimize. One variable at a time:
High-impact elements (test these first):
- Subject lines -- Biggest impact on open rates
- Opening lines -- Biggest impact on read-through rates
- CTAs -- Biggest impact on reply rates
- Send times -- Impacts open rates and reply rates
- From name -- Personal name vs company name vs role-based
Medium-impact elements: 6. Email length -- Short (50 words) vs medium (100 words) vs long (150+ words) 7. Personalization depth -- Light (name + company) vs deep (specific observation + signal) 8. Social proof type -- Customer quote vs metric vs logo vs case study link 9. PS line -- With vs without, different hooks
Low-impact but worth testing after the above: 10. Signature format -- Minimal vs detailed vs with headshot 11. Plain text vs minimal HTML 12. Number of links (0 vs 1 vs 2)
Step 2: Generate Test Variants
For each element, create variants with a clear hypothesis:
Subject Lines (Generate 3-5 variants):
| Variant | Type | Example | Hypothesis |
|---|---|---|---|
| A | Question | "how does [Company] handle [problem]?" | Questions create a cognitive itch that demands resolution |
| B | Stat/Data | "[Company]'s [metric] vs industry benchmark" | Specific data creates curiosity and urgency |
| C | Name-Drop | "[Similar Company] + [Company]" | Familiar names trigger pattern recognition and trust |
| D | Pain-Point | "[specific problem] at [Company]" | Direct relevance to their situation demands attention |
| E | Curiosity | "quick thought about [specific thing]" | Vague but relevant subjects create information gaps |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 139 lines · 22 tokens per session scan A 70a40409f40e
ab-test-generator is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 1,645 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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