Ejecuta cualquier Skill en Manus
con un clic

Ejecuta cualquier Skill en Manus con un clic

doctor

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Actualizado2 de abril de 2026 a las 02:10

Run health diagnostics on the amem memory database. Use when the user wants to check memory health, embedding coverage, stale memories, or diagnose issues.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

amanasmuei

amanasmuei/amem

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Administradores de redes y sistemas informáticosOcupaciones informáticas y matemáticas·SOC 15-1244

SKILL.md

readonly

name	doctor
description	Run health diagnostics on the amem memory database. Use when the user wants to check memory health, embedding coverage, stale memories, or diagnose issues.

Health Diagnostics

Run health diagnostics on the amem memory database.

Instructions

Run via Bash:
```
npx @aman_asmuei/amem doctor
```
Present the results to the user, explaining any issues found.
Key metrics reported:
- Memories — total count
- Embeddings — percentage with semantic search enabled
- Core tier — token budget usage
- Graph edges — knowledge graph connections
- Stale — memories not accessed in 60+ days with low confidence
- Overdue reminders — any reminders past their due date
If issues are found, explain the suggestions:
- Low embedding coverage: restart MCP server or install @huggingface/transformers
- Core tier near budget: review core memories and demote less critical ones
- Stale memories: run memory_consolidate to clean up
- No corrections: suggest storing corrections when the AI makes mistakes

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name	doctor
description	Run health diagnostics on the amem memory database. Use when the user wants to check memory health, embedding coverage, stale memories, or diagnose issues.

Health Diagnostics

Run health diagnostics on the amem memory database.

Instructions

Run via Bash:
```
npx @aman_asmuei/amem doctor
```
Present the results to the user, explaining any issues found.
Key metrics reported:
- Memories — total count
- Embeddings — percentage with semantic search enabled
- Core tier — token budget usage
- Graph edges — knowledge graph connections
- Stale — memories not accessed in 60+ days with low confidence
- Overdue reminders — any reminders past their due date
If issues are found, explain the suggestions:
- Low embedding coverage: restart MCP server or install @huggingface/transformers
- Core tier near budget: review core memories and demote less critical ones
- Stale memories: run memory_consolidate to clean up
- No corrections: suggest storing corrections when the AI makes mistakes