task-search

The task-search skill provides a robust, optimized search interface designed to handle large task datasets with minimal latency. By implementing a 300ms debounce mechanism, the tool ensures that search computations only trigger once the user has finished typing, significantly reducing CPU overhead and providing a smoother interaction experience. It is built to serve power users and project managers who require instantaneous access to specific items within a complex task environment. The underlying algorithm processes three distinct fields—Title, Description, and Tags—applying weighted relevance scores to ensure that the most pertinent results appear at the top. The logic utilizes case-insensitive partial matching combined with an AND-based multi-token filter, requiring tasks to contain all terms provided in the query for inclusion in the results.

• Intelligent search-as-you-type functionality with a configurable 300ms debounce delay to optimize performance and prevent excessive re-renders.

• Sophisticated relevance ranking system that assigns high priority to title matches (100 points for exact, 50 for partial), followed by secondary weighting for descriptions (10 points) and tags (5 points).

• Multi-token query parsing using whitespace-based splitting and logical AND filtering to ensure high-precision results for complex queries.

• Real-time text highlighting capabilities that inject markup into matched content, allowing users to visually identify why a specific task was returned.

• Performance-tuned result sets capped at 50 items, making it ideal for high-density task lists where speed is critical.

• Case-insensitive string matching that ensures intuitive and forgiving search behavior regardless of user input format.

• Users should trigger this skill whenever typing in a global search bar or filtering a task view.

• The skill expects an array of task objects as input and returns a ranked subset based on the provided search query string.

• Ensure the input dataset includes title, description, and tag properties for full search coverage.

• Use this for scenarios requiring high-performance client-side filtering where network requests would be too slow.

• The highlighting feature relies on standard HTML mark tags, making it highly compatible with existing React/web-based UI components.