dev-task-memory

To persist development task state across AI coding sessions, this skill saves task decisions, current state, and next steps to the filesystem using a five-layer architecture combining markdown state files, git checkpoints, and external memory synchronization.

AI loses context after compression or restarts because session memory is volatile. This skill prevents context loss by deterministically recovering task state from filesystem persistence and full-text session search instead of relying on fallible LLM memory.

To resume work-in-progress left by stopped subagents, this skill automatically detects interrupted tasks using a six-step recipe and restores the exact development context needed to continue building, reviewing, and testing without manual intervention.

The best way to hand off long-running software engineering tasks is to generate commit-ready handoff documents containing task decisions and next steps, ensuring the next session or human reviewer can pick up seamlessly after a /new command transition.

You need filesystem state persistence for multi-phase software engineering workflows exceeding thirty minutes that involve planning, building, reviewing, and testing, ensuring seamless recovery across session boundaries without manual user notes.

Yes, this state management approach works with git checkpoints by integrating them into its five-layer persistence architecture to survive any session disruption and ensure deterministic state recovery for long-running development tasks.