memoria-contexto

Store and prioritize non-sensitive user attributes across conversational sessions.

Updated Mar 9, 2026

Search Tags:#context #personalization #memory #privacy #persistent-memory #conversation-history #user-preferences

Ready to run

npx skills add https://github.com/tocadaoncamod/agentes-ia-agno --skill memoria-contexto

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memoria-contexto
Source: https://github.com/tocadaoncamod/agentes-ia-agno/tree/main/.agent/skills/memoria-contexto
Command: npx skills add https://github.com/tocadaoncamod/agentes-ia-agno --skill memoria-contexto

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables conversational agents to remember and reuse user-specific information across sessions so responses feel personalized, context-aware, and continuous without manual repetition.

Core Features & Use Cases

Priority-based memory: Immediately save high-priority items such as user name, active projects, role, timezone, and technology preferences.
Contextual personalization: Use stored preferences to tailor suggestions, code examples, and tone (e.g., prefer Python, technical vs. casual replies).
Privacy and safety rules: Never store sensitive data (passwords, API keys, financial or medical information) and always ask for confirmation before saving.
Use case: Greet returning users by name, recall ongoing project details to continue conversations, and adapt examples to the user's preferred technologies.

Quick Start

Please save my name as João and remember I prefer Python for code examples.

Frequently Asked Questions about memoria-contexto

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I make an AI assistant remember user preferences across conversations?▼

To make an AI assistant remember user preferences, you need persistent memory management to store non-sensitive attributes like names, timezones, and technology choices across sessions for contextual replies. This enables personalized continuity without manual repetition.

What is the best way to store user context and conversation history for personalized responses?▼

Storing user context requires a priority-based memory system that immediately saves high-priority items such as active projects, roles, and technology preferences to enable tailored suggestions and follow-up continuity across conversations.

How do I save user-specific information like my name and preferred programming language for future chats?▼

You can save user-specific information by instructing the assistant directly, such as asking it to save your name and preferred coding language, which it will confirm before storing in persistent memory for future personalization.

How does persistent memory handle sensitive data like passwords and API keys?▼

Persistent memory for conversational agents enforces privacy rules by never storing sensitive data such as passwords, API keys, financial, or medical information, ensuring only non-sensitive user attributes are retained for contextual replies.

Can I use persistent user memory to adapt code examples and tone in conversational assistants?▼

Yes, persistent user memory allows conversational assistants to adapt code examples and tone by recalling stored preferences, such as favoring Python or technical versus casual replies, to provide contextually personalized responses for returning users.

What are the limitations of using persistent memory for personalization in AI assistants?▼

Limitations of persistent memory include excluding sensitive data like passwords and medical information, requiring explicit user confirmation before saving any attributes, and restricting storage to non-sensitive user preferences for contextual continuity.