What problem does it solve? When you return to a codebase, prior work done in AI coding sessions is scattered across Claude Code, Codex, and Cursor transcript files that are too large to read manually. This Skill answers questions about what was tried, decided, or learned in past sessions without loading multi-megabyte JSONL files into context. ## Core Features & Use Cases - Cross-platform session discovery: Locates session files across Claude Code, Codex, and Cursor, filtered by repo, time window, branch, and keyword matches. - Token-safe extraction: Scripts extract metadata, conversation skeletons, and error signals to scratch files so bulk content never floods the orchestrator's context. - Subagent synthesis: Dispatches a historian subagent to read extracted skeletons and return structured findings (what was tried, what failed, key decisions). - Use Case: Ask "what did we try last week to fix the auth middleware token rejection bug?" and get a synthesized summary of relevant prior sessions across all three platforms. ## Quick Start Ask the agent to search your past coding sessions for what was previously attempted on the current bug using the ce-sessions skill.