parse-conversation-timeline

What problem does it solve?

Raw AI conversation logs are often large, unstructured, and difficult to parse, making it challenging for auditors or analysts to extract specific events, track state changes, or understand the chronological flow of an AI agent's work. This complexity hinders effective compliance checks and efficiency analysis. This Skill automates the transformation into actionable data.

Core Features & Use Cases

• Structured Timeline Generation: Converts verbose .jsonl conversation logs into a concise, queryable JSON timeline, making data accessible for analysis.
• Event & File Classification: Automatically categorizes all events (user messages, tool uses, state transitions) and classifies file operations (source, test, infrastructure, documentation) and worktree types.
• Contextual Data Enrichment: Preserves crucial context like working directory, Git branch, and task state for every event, enabling precise auditing and debugging.
• Use Case: An audit skill needs to verify if an agent correctly transitioned through all task states and performed operations in the right worktree. This Skill processes the raw conversation log into a structured timeline, allowing the audit skill to easily query for state transitions, tool uses, and their associated contexts, simplifying complex compliance checks and saving hours of manual log review.

Quick Start

Parse the current session's raw conversation logs into a structured timeline JSON for audit analysis.