continual-learning

name continual-learning description Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents. Continual Learning for AI Coding Agents Your agent forgets everything between sessions. Continual learning fixes that. The Loop Experience → Capture → Reflect → Persist → Apply ↑ │ └───────────────────────────────────────┘ Quick Start Install the hook (one step): cp -r hooks/continual-learning .github/hooks/ Auto-initializes on first session. No config needed. Two-Tier Memory Global ( ~/.copilot/learnings.db ) — follows you across all projects: Tool patterns (which tools fail, which work) Cross-project conventions General coding preferences Local ( .copilot-memory/learnings.db ) — stays with this repo: Project-specific conventions Common mistakes for this codebase Team preferences How Learnings Get Stored Automatic (via hooks) The hook observes tool outcomes and detects failure patterns: Session 1: bash tool fails 4 times → learning stored: "bash frequently fails" Session 2: hook surfaces that learning at start → agent adjusts approach Agent-native (via store_memory / SQL) The agent can write learnings directly: INSERT INTO learnings ( scope , category, content, source) VALUES ( 'local' , 'convention' , 'This project uses Result<T> not exceptions' , 'user_correction' ); Categories: pattern , mistake , preference , tool_insight Manual (memory files) For human-readable, version-controlled knowledge: # .copilot-memory/conventions.md - Use DefaultAzureCredential for all Azure auth - Parameter is semantic _configuration_ name=, not semantic _configuration= Compaction Learnings decay over time: Entries older than 60 days with low hit count are pruned High-value learnings (frequently referenced) persist indefinitely Tool logs are pruned after 7 days This prevents unbounded growth while preserving what matters. Best Practices One step to install — if it takes more than cp -r , it won't get adopted Scope correctly — global for tool patterns, local for project conventions Be specific — "Use semantic_configuration_name=" beats "use the right parameter" Let it compound — small improvements per session create exponential gains over weeks