feat(memory): add lifecycle hooks and vector memory plugin

Add plugin lifecycle hooks infrastructure:
- before_agent_start: inject context before agent loop
- agent_end: analyze conversation after completion
- 13 hook types total (message, tool, session, gateway hooks)

Memory plugin implementation:
- LanceDB vector storage with OpenAI embeddings
- kind: "memory" to integrate with upstream slot system
- Auto-recall: injects <relevant-memories> when context found
- Auto-capture: stores preferences, decisions, entities
- Rule-based capture filtering with 0.95 similarity dedup
- Tools: memory_recall, memory_store, memory_forget
- CLI: clawdbot ltm list|search|stats

Plugin infrastructure:
- api.on() method for hook registration
- Global hook runner singleton for cross-module access
- Priority ordering and error catching

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

extensions/memory/package.json

@@ -0,0 +1,14 @@
+{
+  "name": "@clawdbot/memory",
+  "version": "0.0.1",
+  "type": "module",
+  "description": "Clawdbot long-term memory plugin with vector search and seamless auto-recall/capture",
+  "dependencies": {
+    "@sinclair/typebox": "0.34.47",
+    "@lancedb/lancedb": "^0.15.0",
+    "openai": "^4.77.0"
+  },
+  "clawdbot": {
+    "extensions": ["./index.ts"]
+  }
+}