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.
git clone --depth 1 https://github.com/lucasgaravelli/pm-skills-claude-codeWrote 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/commands/lucasgaravelli/pm-skills-claude-code/ab-test-analysis)<a href="https://agentmods.dev/commands/lucasgaravelli/pm-skills-claude-code/ab-test-analysis"><img src="https://agentmods.dev/badge/commands/lucasgaravelli/pm-skills-claude-code/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/commands/lucasgaravelli/pm-skills-claude-code/ab-test-analysis"><img src="https://agentmods.dev/badge/commands/lucasgaravelli/pm-skills-claude-code/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 | $0.02660 |
| Opus 5 | $0.00000 | $0.01330 |
| Sonnet 5 | $0.00000 | $0.00532 |
| Haiku 4.5 | $0.00000 | $0.00266 |
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 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.
This is a copy
100% identical to ab-test-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ab-test-analysis
O que essa skill faz
Analisa resultados de A/B tests com rigor estatístico: calcula significância, interpreta resultados, checa guardrail metrics e fornece recomendação clara de ação (ship / investigar / estender / parar).
Saída: Analysis Summary com tabela de resultados, interpretação estatística e recomendação.
Quando usar
- Experimento A/B terminou e precisa analisar resultados
- Quer validar se diferença observada é real ou ruído
- Precisa comunicar resultados para stakeholders não-técnicos
- Quer verificar se guardrail metrics foram impactadas
- Teste está rodando e quer fazer interim check (com cuidado)
Input esperado
Mínimo:
- Hipótese: O que esperava acontecer e por quê
- Variantes: Descrição do control e treatment
- Primary metric: Qual métrica principal de sucesso
- Dados: Tamanho da amostra e conversão por variante
- Duração: Quanto tempo o teste rodou
Opcional:
- Guardrail metrics e seus valores
- Segmentação (device, geo, user type)
- Baseline histórico da métrica
- MDE (Minimum Detectable Effect) planejado
- Split ratio (se diferente de 50/50)
Processo de análise
Passo 1 — Entender o experimento
Antes de olhar números, esclarecer:
EXPERIMENT OVERVIEW
═══════════════════════════════════════════════════
Hipótese: [O que esperava e por quê]
Control (A): [descrição]
Treatment (B): [descrição]
Primary metric: [nome e definição]
Guardrail metrics: [lista]
Duração: [X dias]
Split: [% control / % treatment]
Trigger: [quem foi incluído no teste]
Passo 2 — Validar setup
Checklist de sanidade antes de confiar nos resultados:
- Sample size adequado? N suficiente para detectar o MDE desejado
- Duração cobre ciclos de negócio? Mínimo 1-2 semanas, incluindo dia útil + fim de semana
- Randomização correta? Distribuição uniforme entre variantes
- Sample Ratio Mismatch (SRM)? Verificar se split real ≈ split planejado
- Novelty effect descartado? Usuários existentes podem reagir à mudança, não à melhoria
- Peeking controlado? Resultados não foram analisados múltiplas vezes sem correção
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 · 302 lines · 0 tokens per session scan A 86bb8bb43ab7
ab-test-analysis is a command published in the GitHub repository lucasgaravelli/pm-skills-claude-code (20 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,660 tokens. A static security scan graded it A with 0 findings. It is 100% identical to ab-test-analysis, differing in 0 lines, and is treated as a copy.
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