What database does this skill support?

This skill is optimized for Neon PostgreSQL using SQLModel, but the underlying data patterns and role validation logic can be adapted for most SQL-based databases.

Does this handle LLM token limits?

No, this skill focuses exclusively on data persistence and role validation; token counting and context window management should be handled separately in your LLM integration logic.

Why is role ordering important?

Most AI providers (like Anthropic and OpenAI) require a specific message order—typically alternating between user and assistant—and will return errors if messages are out of sequence.

How does it ensure user data privacy?

It implements mandatory user-scoped filtering (ownership checks) at the data layer, ensuring that every database operation is restricted to the authenticated user's ID.

Conversation State Management

Name: Conversation State Management
Author: HasnainCodeHub

byHasnainCodeHub

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データベース管理

Implements robust, database-persisted conversation tracking for stateless AI agents using PostgreSQL.

概要

This skill provides a comprehensive framework for managing AI conversation history in stateless server environments, ensuring seamless continuity across restarts. It automates the creation of message-role architectures, enforces strict role ordering (system-user-assistant), and maintains multi-tenant security through user-scoped isolation. By bridging the gap between stateless API requests and persistent chat contexts, it enables developers to build scalable, reliable AI agents that remember past interactions without relying on ephemeral in-memory storage.

主な機能

Multi-tenant user isolation to ensure private and secure conversation access
Efficient history retrieval patterns for resuming sessions after server restarts
1 GitHub stars
Automated SQLModel data definitions for users, conversations, and messages
Strict role sequence validation (System → User ↔ Assistant) to prevent LLM errors
Stateless request cycle management using Neon PostgreSQL persistence

ユースケース

Building scalable chat applications where server instances are interchangeable
Implementing persistent memory for AI agents across multiple user sessions
Migrating in-memory chat prototypes to production-ready database-backed systems

概要

主な機能

Multi-tenant user isolation to ensure private and secure conversation access
Efficient history retrieval patterns for resuming sessions after server restarts
1 GitHub stars
Automated SQLModel data definitions for users, conversations, and messages
Strict role sequence validation (System → User ↔ Assistant) to prevent LLM errors
Stateless request cycle management using Neon PostgreSQL persistence

ユースケース

Building scalable chat applications where server instances are interchangeable
Implementing persistent memory for AI agents across multiple user sessions
Migrating in-memory chat prototypes to production-ready database-backed systems