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/rice-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/rice-prioritisation)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/rice-prioritisation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/rice-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/rice-prioritisation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/rice-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 | $0.00098 | $0.01675 |
| Opus 5.5 | $0.00039 | $0.00670 |
| Sonnet 5.5 | $0.00020 | $0.00335 |
| Haiku 4.5 | $0.00010 | $0.00168 |
Grade A, and why
rice-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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Habilidad de Priorización RICE
Aplica puntuación RICE consistente y basada en criterios a una lista de funcionalidades o iniciativas para producir una clasificación de priorización objetiva.
Lee desde / Escribe en el Cerebro
Si existe un professional-brain (brain/), toma como base en lugar de volver a preguntar lo que ya sabes:
- Lee primero:
knowledge/strategy.md(para que la clasificación sirva a la dirección), los elementos comoentities/, ehypotheses/de impacto. Ejecutapython3 ../professional-brain/scripts/brain_query.py ./brain "<tema de iniciativa>"y lleva la etiqueta de procedencia de cada hecho — una estimación de impacto es normalmente una[hunch], no[data]. - 📥 Propón al Cerebro: después de producir, propón registrar la decisión de clasificación en
decisions/y las estimaciones de alcance/impacto comohypotheses/etiquetadas por fortaleza de evidencia. Muéstraselas, obtén un sí, luego escribe con../professional-brain/scripts/brain_write.py … --commit(solo append, dry-run por defecto).
Entradas Requeridas
Pide al usuario estas si no se proporcionan:
- Lista de iniciativas o funcionalidades a calificar (nombres y breves descripciones)
- Estimaciones de alcance (usuarios afectados por trimestre — de análisis si está disponible)
- Estimaciones de impacto (usa la escala estándar abajo)
- Estimaciones de esfuerzo (person-meses — de ingeniería si está disponible)
- Trimestre o período de planificación
Definiciones RICE (adapta a tu contexto)
- Alcance (Reach): Número de usuarios afectados por trimestre (usa datos reales de DAU/MAU donde esté disponible)
- Impacto (Impact): Efecto en tu métrica principal — usa escala: 3=masivo, 2=alto, 1=medio, 0.5=bajo, 0.25=mínimo
- Confianza (Confidence): ¿Cuán seguro estamos sobre las estimaciones de R e I? 100%=alta, 80%=media, 50%=baja
- Esfuerzo (Effort): Person-meses requeridos en todas las funciones
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 · 99 lines · 98 tokens per session scan A 4bf0f910e044
rice-prioritisation is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 98 tokens to every session and 1,675 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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