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 frank-luongt/faos-skills-marketplace --skill ab-test-analysisgit clone --depth 1 https://github.com/frank-luongt/faos-skills-marketplaceWrote 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/frank-luongt/faos-skills-marketplace/ab-test-analysis)<a href="https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/ab-test-analysis"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/ab-test-analysis/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/frank-luongt/faos-skills-marketplace/ab-test-analysis"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/ab-test-analysis.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 | $0.00000 | $0.01694 |
| Opus 5 | $0.00000 | $0.00847 |
| Sonnet 5 | $0.00000 | $0.00339 |
| Haiku 4.5 | $0.00000 | $0.00169 |
Grade A, and why
ab-test-analysis 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 12d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: ab-test-analysis description: Analyze A/B test results with statistical rigor — calculate significance, check guardrails, and make ship/extend/stop decisions. Use when evaluating experiment results or interpreting test data. tags: [experimentation, ab-testing, analytics, data-driven]
A/B Test Analysis
Analyze experiment results with statistical rigor and produce a clear Ship / Investigate / Extend / Stop recommendation.
This skill complements ab-test-setup (which handles experiment design). Use this skill when you have results to analyze.
Purpose
Most A/B test interpretations are wrong — teams either call tests too early, ignore guardrail metrics, or ship on directional trends without statistical significance. This skill enforces disciplined analysis.
When to Use
- An A/B test has completed its planned duration
- You have conversion data for control and variant groups
- Stakeholders are asking "did the test win?"
- You need to decide: ship, extend, or kill
When NOT to Use
- Designing or setting up an experiment (use
ab-test-setup) - The test hasn't reached minimum sample size yet
- You're analyzing observational data (not a controlled experiment)
Required Data (Ask If Missing)
| Field | Description |
|---|---|
| Primary metric | What the test is trying to improve (e.g., conversion rate) |
| Control group | Sample size (N) and conversions (C) for the control |
| Variant group | Sample size (N) and conversions (C) for the variant |
| Test duration | How long the test ran |
| Planned duration | How long it was designed to run |
| Guardrail metrics | Metrics that must not degrade (e.g., revenue, page load time) |
| MDE | Minimum Detectable Effect used in power calculation |
Analysis Process
Step 1: Validate the Setup
Before analyzing results, check:
- Sample size adequate? Compare actual N to planned N from power analysis
- Duration sufficient? Must cover at least 1–2 full business cycles (e.g., weekday + weekend)
- SRM check? Sample Ratio Mismatch — control and variant should have ~equal N (within 1%). If skewed, the test is invalid.
- No novelty effects? If you can, check early vs. late behavior. New UI elements get more clicks initially.
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.
- 12d ago First seen · 206 lines · 0 tokens per session scan A 8680e7a407d4
ab-test-analysis is a skill published in the GitHub repository frank-luongt/faos-skills-marketplace (33 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,694 tokens. 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 2026-08-30.
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