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 gtmagents/gtm-agents --skill offer-testinggit clone --depth 1 https://github.com/gtmagents/gtm-agentsWrote 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/gtmagents/gtm-agents/offer-testing)<a href="https://agentmods.dev/skills/gtmagents/gtm-agents/offer-testing"><img src="https://agentmods.dev/badge/skills/gtmagents/gtm-agents/offer-testing/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/gtmagents/gtm-agents/offer-testing"><img src="https://agentmods.dev/badge/skills/gtmagents/gtm-agents/offer-testing.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 | $0.00018 | $0.00249 |
| Opus 5 | $0.00009 | $0.00125 |
| Sonnet 5 | $0.00004 | $0.00050 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
offer-testing 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.
What it actually says
Offer Testing Playbooks Skill
When to Use
- Planning subject line/CTA/offer tests across email, ads, or landing pages.
- Validating new positioning or pricing language.
- Running copy refresh cycles for campaigns.
Framework
- Hypothesis – statement of expected lift + rationale.
- Variable Selection – hook, CTA, body copy, offer framing, proof element.
- Segmentation – define audience splits and holdouts.
- Metrics – primary KPI + guardrails (opens, CTR, CVR, CPL, unsub, spam).
- Analysis – statistical significance (chi-square, z-test) or Bayesian approach.
Templates
- Experiment brief (variable, control, variant, KPI, sample size, duration).
- Results report (metric table, significance, insight, next steps).
- Prioritization matrix (ICE/RICE scoring).
Tips
- Limit to one variable per test to isolate learnings.
- Ensure minimum sample sizes per channel before declaring winners.
- Log tests and learnings in a shared repository.
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 · 31 lines · 18 tokens per session scan A f1f98c00cfaf
offer-testing is a skill published in the GitHub repository gtmagents/gtm-agents (399 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 249 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 2026-09-03.
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