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 churn-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/churn-analysis)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/churn-analysis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/churn-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/churn-analysis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/churn-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 | $0.00099 | $0.03075 |
| Opus 5.5 | $0.00040 | $0.01230 |
| Sonnet 5.5 | $0.00020 | $0.00615 |
| Haiku 4.5 | $0.00010 | $0.00308 |
Grade A, and why
churn-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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill de Análisis de Churn
Produce un análisis de churn estructurado que vaya más allá de la tasa titular — identificando por qué se van los clientes, cuáles son los segmentos más en riesgo, y qué intervenciones tendrán el mayor impacto en la retención.
Lee de / Escribe en el Brain
Si existe un professional-brain (brain/), toma como base en lugar de volver a preguntar por lo que ya sabes:
- Lee primero:
context.md(definiciones de métricas — qué significa "churn" aquí),knowledge/yentities/de segmentos relacionados. Ejecutapython3 ../professional-brain/scripts/brain_query.py ./brain "churn"y mantén la etiqueta de procedencia de cada hecho. - 📥 Propón al Brain: después de producir, propón registrar el hallazgo titular de retención a
knowledge/([data]), cualquier decisión de retención adecisions/y los drivers en riesgo comohypotheses/. Muéstralos, obtén confirmación, luego escribe con../professional-brain/scripts/brain_write.py … --commit(append-only, dry-run por defecto).
Inputs Requeridos
Pregunta por estos si no están ya disponibles:
- Período de tiempo siendo analizado (p. ej. Q1, últimos 12 meses)
- Total de clientes al inicio del período y clientes que hicieron churn
- ARR o ingresos perdidos por churn
- Datos de razones de churn — resultados de encuestas de salida, notas de CSM, datos de soporte o razones de pérdida de ventas
- Segmentos de clientes — por tier, industria, cohorte o línea de producto
- Tasa de retención actual si se conoce
- Cualquier cambio reciente — precios, producto, modelo de soporte — que haya podido afectar al churn
Categorías de Churn
Siempre clasifica el churn antes de analizarlo:
| Categoría | Definición |
|---|---|
| Voluntario — evitable | El cliente se fue debido a un problema que habríamos podido solucionar (gaps de producto, onboarding pobre, fallos relacionales) |
| Voluntario — inevitable | El cliente se fue por razones fuera de nuestro control (cortes de presupuesto, adquisición, cierre de empresa) |
| Involuntario | Fallo de pago, no renovación de contrato por error, error administrativo |
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 · 220 lines · 99 tokens per session scan A 398be7f01594
churn-analysis 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 3,075 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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