temporal reasoning sleuth

The problem

Reconstructing events from messy logs, contradictory documents, or vague narratives is prone to human error. Developers often struggle to normalize relative time expressions or detect logical impossibilities in complex event sequences.

What it does

• Extracts and normalizes absolute, relative, and approximate time expressions into a defensible model.
• Detects real, apparent, and unresolved temporal contradictions or ordering cycles.
• Resolves event identity to prevent duplicate counts or misattributed timestamps.
• Calculates duration and interval ranges while preserving original precision and timezone semantics.
• Generates evidence-linked timelines that distinguish between established facts and assumptions.

Why this beats prompting it yourself

General LLMs tend to "smooth out" timelines, often inventing dates to fill gaps or ignoring timezone shifts. This skill enforces a rigorous partial-order model that treats uncertainty as first-class data and refuses to manufacture precision where none exists.

Use cases

• Incident post-mortems for distributed systems where logs have skewed timestamps.
• Legal or forensic document analysis to identify conflicting witness accounts.
• Dependency reasoning for complex project plans with hard and soft constraints.
• Version and change-log audits to track state transitions over time.

Known limitations

Cannot resolve conflicts where evidence is entirely missing. Requires explicit timezone data for high-precision duration calculations.