What problem does it solve? Ending a coding session cleanly requires several manual steps: checking for uncommitted changes, archiving finished tasks, and logging what was accomplished. This Skill automates that wrap-up sequence so nothing is forgotten and the project history stays consistent. ## Core Features & Use Cases - Dirty-tree classification: Inspects git status output, filters out Trellis-managed paths, and decides whether remaining changes belong to the current task or parallel work, blocking the wrap-up if task code is uncommitted. - Task archival: Archives the active task (plus any other completed tasks the user confirms) via task.py archive, producing auto-committed chore(task): archive commits. - Session journaling: Records a session entry with title, commit hashes, and summary through add_session.py, producing a chore: record journal commit. - Use Case: After finishing a feature and committing code in the workflow's commit phase, run this Skill to archive the task and log the session so the next session starts with full context. ## Quick Start Ask the AI to finish the work session by archiving the current task and recording the session journal.
