PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill ab-test-plannergit clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills/ab-test-planner)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/ab-test-planner"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ab-test-planner/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/mohitagw15856/pm-claude-skills/ab-test-planner"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ab-test-planner.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 | $0.00099 | $0.02299 |
| Opus 5.5 | $0.00040 | $0.00920 |
| Sonnet 5.5 | $0.00020 | $0.00460 |
| Haiku 4.5 | $0.00010 | $0.00230 |
Grade A, and why
ab-test-planner 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 2d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill de Planificador de Pruebas A/B
Diseña experimentos que produzcan resultados confiables — no solo señales direccionales. Cada prueba incluye hipótesis, métricas de éxito, tamaño de muestra, duración e interpretación de resultados.
Entradas Requeridas
Trabaja con lo que se proporciona. Si falta algo de esto, produce igualmente el resultado completo: rellena cada vacío con un supuesto razonable marcado (asumido, confirma) y lista esos supuestos al final. Haz una pregunta solo cuando el tema central en sí esté ausente (no hay nada sobre lo que escribir).
- Qué se está probando (característica, cambio de UI, copia, precios, paso de onboarding)
- Hipótesis (o ayuda a formular una)
- Métrica primaria (tasa de conversión, click-through, tasa de finalización, etc.)
- Tasa base y efecto mínimo detectable (MDE)
- Usuarios elegibles diarios (para calcular duración)
Lista de Verificación de Diseño de Experimento
Antes de ejecutar cualquier prueba, confirma:
- Hipótesis clara con dirección predicha
- Métrica primaria única (más hasta 2 métricas de seguridad)
- Efecto mínimo detectable (MDE) definido
- Tamaño de muestra calculado
- Duración de prueba estimada
- Segmento aislado (sin superposición con otras pruebas en ejecución)
- Plan de reversión definido
Plantilla de Hipótesis
"Creemos que [cambio] causará que [métrica primaria] [aumente/disminuya] en [X%] para [segmento de usuario], porque [justificación basada en datos o perspectiva]."
Nunca ejecutes una prueba sin una hipótesis direccional. "Veamos qué pasa" no es una hipótesis.
Lógica de Calculadora de Tamaño de Muestra
Usa esta fórmula (proporciona el resultado, no la fórmula, al usuario):
- Tasa de conversión base: Tasa actual de la métrica primaria
- MDE: Cambio más pequeño que vale la pena detectar (recomendado incremento relativo de 10–20% para la mayoría de características)
- Poder estadístico: 80% (estándar)
- Nivel de significancia: 95% (p < 0.05)
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.
- 2d ago First seen · 148 lines · 99 tokens per session scan A e9a25c329408
ab-test-planner is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 2,299 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-12.
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