temporal-reasoning-sleuth
Give AI agents the ability to trace decision chains, reconstruct causal sequences, and reason over complex event timelines spanning months or years.
skill install https://www.promptspace.in/skills/temporal-reasoning-sleuthWhat 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:
Sequence queries — What happened between A and B?
Causal queries — What caused X? What led to Y?
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.
Use cases
- Answering "what decisions led to X?" over engineering or org history
- Post-incident analysis — reconstructing the causal chain of a system failure
- Architecture decision record (ADR) querying across months of changes
- Compliance and audit trails — explaining why a policy or system state exists
- Product retrospectives — tracing how a feature or strategy evolved over time
Example
Prompt
Output
Query: "What decisions led us to adopt OAuth2 for authentication?" CAUSAL CHAIN FOR: OAuth2 adoption (auth-service) ================================================= [Step 1 — 2025-03-14] Incident: Auth service credential breach via legacy token endpoint. Actors: Security team, Platform lead. Impact: 3-hour outage, mandatory security review initiated. [Step 2 — 2025-03-21 → caused by Step 1] Decision: Security review mandated token endpoint deprecation within 90 days. Rationale: Legacy tokens lacked expiry and rotation controls. [Step 3 — 2025-05-08 → caused by Step 2] Decision: OAuth2 selected over SAML after vendor evaluation. Rationale: Better library support, aligns with existing API gateway. [Step 4 — 2025-06-01 → caused by Step 3] Event: OAuth2 migration completed and legacy endpoint retired.
Known limitations
Counterfactual query quality depends on how completely causal edges were populated at ingestion time. Sparse or poorly-linked event graphs will produce shallow causal chains. This skill provides the reasoning architecture — it does not auto-populate causal relationships from raw documents (see synthesizing-institutional-knowledge for that).