What problem does it solve? AI coding agents forget everything between sessions, forcing users to repeat corrections and re-explain project conventions. This Skill sets up continual learning infrastructure so agents capture, persist, and apply learnings automatically. ## Core Features & Use Cases - Two-Tier Memory: Global learnings (tool patterns, cross-project conventions) stored in ~/.copilot/learnings.db, and local project-specific learnings stored in .copilot-memory/learnings.db. - Automatic Capture via Hooks: A hook observes tool outcomes, detects failure patterns, and surfaces relevant learnings at the start of the next session. - Agent-Native and Manual Storage: Agents can write learnings directly via SQL (categories: pattern, mistake, preference, tool_insight), while humans can maintain version-controlled markdown memory files. - Use Case: After your agent repeatedly fails with a bash command in one session, the hook stores that pattern; in the next session the learning is surfaced so the agent adjusts its approach without being told again. ## Quick Start Ask the agent to install the continual-learning hook by copying hooks/continual-learning into .github/hooks/ so it auto-initializes on the first session.