cause and effect timeline analyzer

The problem

Fragmented logs, conflicting testimonies, and incomplete event data make it difficult to determine why a failure occurred. Teams often confuse temporal sequence with actual causation, leading to ineffective post-mortems and recurring incidents.

What it does

• Normalizes messy event data into a unified, UTC-aligned temporal anchor list.
• Categorizes events as Decisions, Triggers, Contributing Factors, Consequences, or Turning Points.
• Maps relationships between events, distinguishing between sequence, correlation, and direct causation.
• Tests alternative hypotheses and applies Occam's razor to rank the most plausible causal chains.
• Identifies evidence gaps and explicitly flags the confidence level of every causal claim.

Why this beats prompting it yourself

Generic prompts often hallucinate connections to create a cohesive story. This skill enforces a multi-phase analytical rigor that prioritizes evidence over narrative. It forces the identification of gaps and the evaluation of alternative explanations that simple prompts usually skip.

Use cases

• Conducting technical incident retrospectives or root cause analyses (RCA).
• Reconstructing historical project sequences from scattered Slack messages and Jira tickets.
• Analyzing complex system failures where multiple contributing factors overlap.
• Auditing decision-making processes to identify critical turning points in a project lifecycle.

Known limitations

Requires an initial set of raw event data or logs to function. It cannot invent missing evidence but will explicitly flag where data is absent.