feat(evolution): add self-evolution subsystem

Add a self-evolution subsystem that reviews idle conversations in an
isolated agent and durably learns from them — patching/creating skills,
finishing unfinished tasks, and backfilling missed memory.

- Trigger: background idle scan, fires when a session is idle >= N min AND
  (>= N turns OR context usage > 80%). In-memory cursor reviews only new
  messages so a session never re-learns old content.
- Isolated review agent: same model, restricted toolset, hard write-guard
  confining edits to the workspace (built-in skills are protected).
- Safety: file-level backup before edits + evolution_undo tool; notify the
  user ONLY when a workspace file actually changed (no-nag rule); capped
  concurrency.
- Records to memory/evolution/<date>.md, surfaced in the memory UI's
  renamed "Self-Evolution" tab (merged with dream diaries).
- Hide internal [SCHEDULED]/[EVOLUTION]/backup_id markers from chat history
  display (also fixes scheduler marker leakage) while keeping them in stored
  content for undo.
- Flat config: self_evolution_enabled (default off until release),
  self_evolution_idle_minutes (15), self_evolution_min_turns (6).
- Tests: tests/test_evolution.py (stub + real model modes, 7 scenarios).

agent/tools/evolution_undo/evolution_undo.py

@@ -0,0 +1,58 @@
+"""Evolution undo tool.
+
+Lets the main chat agent roll back a previous self-evolution when the user asks
+("undo the last learning"). The rollback itself is a deterministic FILE RESTORE
+from the snapshot taken before the evolution — the model only supplies the
+backup_id it reads from the [EVOLUTION] record in the conversation. No LLM-driven
+re-editing is involved, so a restore can never make things worse.
+"""
+
+from agent.tools.base_tool import BaseTool, ToolResult
+
+
+class EvolutionUndoTool(BaseTool):
+    """Restore memory/skill files to the state before a self-evolution."""
+
+    name: str = "evolution_undo"
+    description: str = (
+        "Undo a previous self-evolution (self-learning) by restoring the "
+        "memory/skill files to their state before that learning. Use this when "
+        "the user asks to undo / revert / roll back the last self-learning. "
+        "Find the backup_id in the most recent [EVOLUTION] record in the "
+        "conversation and pass it here."
+    )
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "backup_id": {
+                "type": "string",
+                "description": (
+                    "The backup_id from the [EVOLUTION] record to restore "
+                    "(e.g. '20260607-155551-850')."
+                ),
+            }
+        },
+        "required": ["backup_id"],
+    }
+
+    def execute(self, args: dict):
+        backup_id = (args.get("backup_id") or "").strip()
+        if not backup_id:
+            return ToolResult.fail("Error: backup_id is required")
+        try:
+            from agent.memory.config import get_default_memory_config
+            from agent.evolution.backup import restore_backup
+
+            workspace_dir = get_default_memory_config().get_workspace()
+            ok = restore_backup(workspace_dir, backup_id)
+            if ok:
+                return ToolResult.success(
+                    f"Restored memory/skills to the state before evolution "
+                    f"{backup_id}. The previous self-learning has been undone."
+                )
+            return ToolResult.fail(
+                f"Could not find or restore backup {backup_id}. It may have "
+                f"expired or already been rolled back."
+            )
+        except Exception as e:
+            return ToolResult.fail(f"Error during undo: {e}")