What problem does it solve? AI coding agents leave behind scattered JSONL session logs that are hard to search and interpret. This Skill normalizes those logs so you can reconstruct what happened in past sessions, compare approaches, and learn from recurring patterns or failures. ## Core Features & Use Cases - Session Reconstruction: Rebuild the goals, decisions, and outcomes of named Codex or Claude Code sessions from their JSONL transcripts. - Cross-Session Pattern Analysis: Explore recurring topics, preferences, workflows, and changes over time across many sessions, with deduplication of forked transcripts. - Failure and Workaround Analysis: Trace attempted actions, failures, diagnoses, and successful workarounds to derive durable improvements for agent guidance. - Use Case: Ask what your agent did last week on a specific project, filter sessions by working directory and topic, and receive an evidence-backed summary with coverage caveats. ## Quick Start Analyze my Codex and Claude Code sessions from the past week and summarize the recurring themes and decisions.