What This Skill Does

AI agents fail on temporal queries — not because they lack intelligence, but because they receive the wrong kind of context. This skill teaches your agent the architecture patterns and retrieval strategies needed to reason accurately over event timelines and causal chains.

Problems It Solves

• "Lost in the middle" failures — when agents ignore key events buried in long chronological dumps.

• Context poisoning — when events retrieved without causal context lead to wrong conclusions.

• Unanswerable history questions — "What decisions led to X?" "How did this situation evolve?" "What if we had done Y instead?"

What You Get

The skill covers three temporal query types your agent must handle:

1. Sequence queries — What happened between A and B?

2. Causal queries — What caused X? What led to Y?

3. Counterfactual queries — What if decision D had been different?

It then provides concrete architecture patterns: event graphs with timestamped causal edges, pre-computed causal chain indexes, and windowed context synthesis that compresses distant history to fit context windows without losing critical signal.

Who Should Use This

Teams building AI agents over organizational knowledge bases, incident histories, architecture decision records, or any system where understanding why something happened is as important as knowing what happened.