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 data-analysis-standardgit 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/data-analysis-standard)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/data-analysis-standard"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/data-analysis-standard/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/data-analysis-standard"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/data-analysis-standard.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 | $0.00088 | $0.01956 |
| Opus 5.5 | $0.00035 | $0.00782 |
| Sonnet 5.5 | $0.00018 | $0.00391 |
| Haiku 4.5 | $0.00009 | $0.00196 |
Grade A, and why
data-analysis-standard 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill de Análisis de Datos Estándar
Convierte números en bruto en decisiones de producto. Estructura cada análisis con una pregunta clara, metodología, hallazgo y acción recomendada.
Marco de Análisis: El Método de las 4 Preguntas
Todo análisis comienza aquí:
- ¿Qué cambió? (describe la métrica y su movimiento)
- ¿Por qué cambió? (causa raíz — segmento, paso de embudo, cohorte, canal)
- ¿Y qué? (impacto en el negocio o producto)
- ¿Ahora qué? (acción recomendada con nivel de confianza)
Nunca entregues datos sin responder las cuatro. Un gráfico sin narrativa no es un análisis.
Plantilla de Triaje de Métrica
Úsala cuando una métrica se ha movido inesperadamente:
MÉTRICA: [Nombre]
MOVIMIENTO: [X% de cambio en Y período]
LÍNEA BASE: [Cuál era lo normal]
VERIFICACIÓN DE SEGMENTACIÓN:
- ¿Por plataforma (iOS / Android / Web)?
- ¿Por cohorte de usuario (nuevos / retornantes / usuarios avanzados)?
- ¿Por canal de adquisición?
- ¿Por geografía?
- ¿Por plan/tier?
HIPÓTESIS DE CAUSA RAÍZ:
1. [Explicación más probable] — Evidencia: [punto de datos]
2. [Explicación alternativa] — Evidencia: [punto de datos]
3. [Descartar] — Eliminado porque: [razón]
CONCLUSIÓN: [Respuesta de una oración a "¿por qué cambió esto?"]
CONFIANZA: [Alta / Media / Baja] — basado en [datos disponibles]
Estructura de Análisis de Embudo
| Etapa | Métrica | Actual | Referencia/Objetivo | Caída % | Notas |
|---|---|---|---|---|---|
| [Parte superior del embudo] | [Usuarios] | [N] | [N] | — | |
| [Paso 2] | [Usuarios] | [N] | [N] | [X%] | |
| [Paso 3] | [Usuarios] | [N] | [N] | [X%] | |
| [Conversión] | [Usuarios] | [N] | [N] | [X%] |
Mayor caída: [Paso X → Paso Y] — Hipótesis: [razón] Investigación recomendada: [consulta específica o test]
Directrices de Análisis de Cohortes
Siempre define:
- Definición de cohorte: [Lo que agrupa a usuarios — semana de signup, primera acción, tipo de plan]
- Métrica de retención: [Qué cuenta como retención — login, acción principal, ingresos]
- Ventana de retención: [D1, D7, D30, W4, M3, etc.]
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 · 161 lines · 88 tokens per session scan A 82baa6954afc
data-analysis-standard is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 88 tokens to every session and 1,956 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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