What problem does it solve?
Persistent AI agents receive a flood of ambient signals and changing user context that can bloat memory, trigger unnecessary interruptions, and degrade reasoning quality; this Skill defines what to observe, how to filter it, and when to surface it so the agent stays relevant and efficient.
Core Features & Use Cases
- Retrieval strategy selection: Templates for recency-first, relevance-first, frequency-based, and event-driven retrieval budgets tied to task type.
- Ambient data filtering: Rules for screen, audio, and app-change observations with relevance scoring, de-duplication, and retention gating.
- Trigger urgency tiers: Silent, badge, passive, active, and interrupt mappings based on event type and context to avoid trigger spam.
- Failure modes & quality gates: Detection and fixes for schema bloat, retrieval thrashing, ambient noise flooding, and staleness with measurable gates.
- Worked example: A token-budgeted troubleshooting flow that combines semantic reranking, temporal search, and ambient observation to debug a billing bug without exceeding context limits.
Quick Start
Ask the agent what it currently observes and request a one-paragraph summary of high-signal items to seed its short-term context.
