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 cohort-analysisgit 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/cohort-analysis)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/cohort-analysis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/cohort-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/mohitagw15856/pm-claude-skills/cohort-analysis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/cohort-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 | $0.00093 | $0.04482 |
| Opus 5.5 | $0.00037 | $0.01793 |
| Sonnet 5.5 | $0.00019 | $0.00896 |
| Haiku 4.5 | $0.00009 | $0.00448 |
Grade A, and why
cohort-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 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill de Análisis de Cohortes
Produce un análisis de cohortes estructurado que cubre curvas de retención, estimación de LTV, segmentación comportamental, indicadores principales de churn y intervenciones priorizadas. El resultado está listo para presentar a liderazgo de producto o compartir con equipos de growth y datos.
Entradas Requeridas
Solicita cualquiera de estas que falte antes de empezar. No inventes números, benchmarks ni detalles de esquema.
| Entrada | Qué preguntar si falta |
|---|---|
| Objetivo del análisis | Mejora de retención / modelado de LTV / segmentación comportamental / predicción de churn — elige un objetivo principal |
| Producto o feature | ¿Qué se está analizando? |
| Definición de cohorte | ¿Qué agrupa a los usuarios en una cohorte? (mes de adquisición, canal de registro, tier de plan, fecha de adopción de feature) |
| Ventana de observación | ¿Cuántos períodos seguir? (p. ej. 12 meses, 8 semanas) |
| Métrica clave | ¿Qué se mide por cohorte? (tasa de retención, ingresos, puntuación de engagement, uso de features) |
| Datos disponibles | Pega esquema, nombres de tablas, o describe qué métricas existen — no asumas |
| Baseline o meta | ¿Hay benchmarks de retención existentes o metas para comparar? |
Si el usuario no puede suministrar datos reales, produce el marco con placeholders claramente marcados ([X%], [£X], [N usuarios]) y nota qué secciones requieren datos reales para completarse.
Proceso
Sigue estos pasos en orden:
- Confirma entradas — surfacea cualquier ambigüedad antes de producir output (p. ej. definiciones de cohorte conflictivas, ventana de observación faltante).
- Define cohortes — establece evento de entrada, criterios de salida y exclusiones para que las cohortes sean mutuamente excluyentes.
- Construye tabla de retención — completa o templatea la matriz período a período; identifica el período de meseta.
- Proyecta LTV — usa solo datos de retención observados; marca si los datos son insuficientes.
- Segmenta por comportamiento — crea segmentos comportamentales mutuamente excluyentes; identifica el threshold de activación.
- Surface indicadores principales de churn — lista señales observables con tiempo de anticipación y mapeo de intervención.
- Compara cohortes en el tiempo — evalúa si los cambios de producto se muestran en cohortes más nuevas.
- Prioriza recomendaciones — vincula cada recomendación a un hallazgo de cohorte o segmento específico.
- Proporciona referencia SQL — adapta la query template al esquema real del usuario si se suministra.
- Ejecuta quality checks — verifica todos los ítems del checklist antes de entregar output.
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 · 288 lines · 93 tokens per session scan A ffbbee662e13
cohort-analysis is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 4,482 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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