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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsnpx agentmods add skills/mohitagw15856/pm-claude-skills/feature-prioritisationWrote 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/feature-prioritisation)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/feature-prioritisation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/feature-prioritisation/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/feature-prioritisation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/feature-prioritisation.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 | $0.00103 | $0.02203 |
| Opus 5.5 | $0.00041 | $0.00881 |
| Sonnet 5.5 | $0.00021 | $0.00441 |
| Haiku 4.5 | $0.00010 | $0.00220 |
Grade A, and why
feature-prioritisation 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill de Priorización de Características
Aplica el marco de priorización correcto a cualquier backlog y produce una clasificación clara y defendible con justificación — no solo una lista ordenada.
Inputs Requeridos
Trabaja con lo que se proporciona. Si falta alguno de estos, aun así produce el output completo: rellena cada vacío con un supuesto razonable marcado (asumido, confirmar) y lista esos supuestos al final. Solo haz una pregunta cuando el tema central en sí está ausente (no hay nada sobre lo que escribir).
- Lista de características o iniciativas a priorizar
- Objetivo o métrica siendo priorizado (OKR, lanzamiento, sprint)
- Marco preferido (o recomienda basándote en el contexto abajo)
- Datos del equipo: estimaciones de alcance, estimaciones de esfuerzo, velocidad (para RICE)
Guía de Selección de Marco
Pregunta al usuario qué marco prefiere, o recomienda basándote en el contexto:
| Situación | Marco Recomendado |
|---|---|
| Necesitas una puntuación rápida y basada en datos | RICE |
| Reunión de alineación de stakeholders | MoSCoW |
| Entender la satisfacción del cliente vs expectativas | Kano |
| Startup en etapa temprana, decisiones rápidas | ICE |
| Identificar necesidades de clientes desatendidas | Opportunity Scoring |
| Decisiones de portafolio estratégico | Value vs Effort Matrix |
Puntuación RICE
Fórmula: (Alcance × Impacto × Confianza) ÷ Esfuerzo
| Factor | Definición | Escala |
|---|---|---|
| Alcance | Usuarios impactados por trimestre | Número real |
| Impacto | Efecto en el objetivo por usuario | 0.25 / 0.5 / 1 / 2 / 3 |
| Confianza | ¿Cuán seguro estás? | 50% / 80% / 100% |
| Esfuerzo | Personas-mes requeridas | Número real |
Tabla de output:
| Característica | Alcance | Impacto | Confianza | Esfuerzo | Puntuación RICE | Prioridad |
|---|
Método MoSCoW
Clasifica cada característica como:
- Must Have — innegociable para el lanzamiento/sprint; el producto falla sin ella
- Should Have — importante pero no crítico; existen alternativas
- Could Have — agradable de tener; incluir solo si hay tiempo
- Won't Have (esta vez) — explícitamente fuera de alcance ahora; puede revisarse después
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 · 178 lines · 103 tokens per session scan A 7d683d4a8f42
feature-prioritisation is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 2,203 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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