accelint-tanstack-query-best-practices

What it does

This skill provides a curated set of best practices for configuring and utilizing TanStack Query within AI agent workflows. It ensures agents handle data fetching, caching, and synchronization efficiently while preventing common pitfalls like redundant requests or stale data.

When to use it

• Building AI agents that require real-time access to external APIs or databases without overwhelming the backend.
• Implementing robust error handling and retry logic for unreliable network connections in agent tasks.
• Optimizing performance by configuring cache invalidation strategies to keep agent context fresh.
• Structuring agent codebases to separate data fetching logic from business logic for better maintainability.

Key capabilities

• Configuration of query keys, base URLs, and default options for consistent API interactions.
• Implementation of caching strategies to reduce latency and network overhead.
• Setup of error boundaries and retry mechanisms for resilient data retrieval.
• Guidance on synchronizing server state with local agent context.

Example prompts

• "Configure TanStack Query for an AI agent that fetches user profile data from a REST API."
• "Set up caching and invalidation rules to ensure my agent always has the latest inventory levels."
• "Implement retry logic for an agent making frequent weather API calls during a storm event."

Tips & gotchas

Ensure your agent environment includes the necessary TanStack Query dependencies before applying these best practices. Be mindful that aggressive caching can lead to stale data if not paired with appropriate invalidation triggers.