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 retention-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/retention-analysis)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/retention-analysis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/retention-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/retention-analysis"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/retention-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 | $0.00086 | $0.01763 |
| Opus 5.5 | $0.00034 | $0.00705 |
| Sonnet 5.5 | $0.00017 | $0.00353 |
| Haiku 4.5 | $0.00009 | $0.00176 |
Grade A, and why
retention-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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill de Análisis de Retención
Diagnostica por qué los usuarios se van, identifica qué los mantiene y recomienda intervenciones específicas y testables — no sugerencias vagas como "mejorar onboarding".
Fundamentos de Retención
La curva de retención tiene dos componentes:
- Inclinación de la caída inicial (D1–D7) — problema de onboarding
- Nivel base a largo plazo — indicador de product-market fit
Un producto con PMF tiene una curva de retención que se aplana. Si tiende a cero, tienes un problema de PMF, no de onboarding. Nombra esta distinción explícitamente.
Definiciones de Métricas de Retención
| Métrica | Fórmula | Qué te dice |
|---|---|---|
| Retención D1 | Usuarios que regresan el día 2 ÷ usuarios nuevos día 1 | Calidad de la primera experiencia |
| Retención D7 | Usuarios activos el día 8 ÷ usuarios que se unieron hace 7 días | Formación temprana de hábito |
| Retención D30 | Usuarios activos el día 31 ÷ usuarios que se unieron hace 30 días | Señal de product-market fit |
| Ratio DAU/MAU | Usuarios activos diarios ÷ usuarios activos mensuales | Stickiness (>20% bueno, >50% excelente) |
| Churn Rate | Usuarios perdidos en período ÷ usuarios al inicio del período | Mensual o anual |
| Net Revenue Retention | MRR al final del período ÷ MRR al inicio (misma cohorte) | Salud de ingresos incluyendo expansion |
Marco de Investigación de Retención
Paso 1: Segmenta el problema
No analices "retención" — analiza retención para cohortes específicas:
- Usuarios nuevos vs retornantes
- Pagados vs free
- Canal de adquisición (orgánico vs pagado vs referral)
- Onboarding completado vs no completado
- Uso de features (power users vs lurkers)
Paso 2: Encuentra los puntos de inflexión
¿Dónde ocurre la caída? ¿D1? ¿D7? ¿Mes 3?
- Caída D1 → Experiencia de primera sesión
- Caída D7 → Habit loop no formado
- Caída D30 → Valor no entregado en profundidad
- Caída Mes 3+ → Aburrimiento, competencia o evento de ciclo de vida
Paso 3: Identifica la correlación del "aha moment"
¿Qué comportamiento temprano predice retención a largo plazo?
- Ejecuta correlación: usuarios que hicieron [X] en los primeros 7 días vs retención de 30 días
- Patrones comunes: conectaron una integración, invitaron a un compañero, completaron una acción core N veces
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 · 144 lines · 86 tokens per session scan A 21a9d950d5aa
retention-analysis is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 1,763 once invoked, about $0.0003 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…