generate-suggestions

To automate follow-up task suggestions, apply a context-aware synthesis tool that analyzes task.json, research notes, results, and logs to generate prioritized, deduplicated candidate tasks. This process outputs a structured suggestions.json file for verification.

Context-aware task generation analyzes current task artifacts like task.json, research notes, and logs to produce candidate suggestions. It synthesizes project context to surface new experiments, techniques, datasets, or evaluations automatically.

To prevent duplicate tasks during suggestion generation, the system validates candidates against the official suggestions specification and deduplicates them against existing tasks and suggestions. This ensures only non-duplicate, prioritized items are written to suggestions.json.

Yes, you can generate task suggestions from research notes and project logs. The system synthesizes these artifacts along with task.json and results to produce candidate, non-duplicate, prioritized suggestions for experiments, techniques, datasets, or evaluations.

The best way to prioritize follow-up tasks is using a generation tool that analyzes task context and applies prioritization tagging with recommended task types. This streamlines workflow automation by outputting ranked, deduplicated suggestions.

Yes, you need a completed task folder containing artifacts like task.json, research notes, results, plans, or logs. The system synthesizes these outputs and project context to generate candidate, non-duplicate, prioritized suggestions for follow-up tasks.